#Scatter plots with Plotly Express
# x and y given as array_like objects
import plotly.express as px
fig = px.scatter(x=[0, 1, 2, 3, 4], y=[0, 1, 4, 9, 16])
fig.show()
# x and y given as DataFrame columns
import plotly.express as px
df = px.data.iris() # iris is a pandas DataFrame
fig = px.scatter(df, x="sepal_width", y="sepal_length")
fig.show()
#Setting size and color with column names
import plotly.express as px
df = px.data.iris()
fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species",
size='petal_length', hover_data=['petal_width'])
fig.show()
import plotly.express as px
df = px.data.iris()
fig = px.scatter(df, x="sepal_width", y="sepal_length", color='petal_length')
fig.show()
import plotly.express as px
df = px.data.iris()
fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species", symbol="species")
fig.show()
#Scatter plots and Categorical Axes
import plotly.express as px
df = px.data.medals_long()
fig = px.scatter(df, y="nation", x="count", color="medal", symbol="medal")
fig.update_traces(marker_size=10)
fig.show()
#Grouped Scatter Points
import plotly.express as px
df = px.data.medals_long()
fig = px.scatter(df, y="count", x="nation", color="medal")
fig.update_traces(marker_size=10)
fig.update_layout(scattermode="group")
fig.show()
#You can configure the gap between groups of scatter points using
#scattergap. Here we set it to 0.75, which brings the points closer together by allocating more space to the gap between groups. If you don't set scattergap, a default value of 0 is used, unless you have bargap set.
#If you have bargap set, the scattergap defaults to that value.
import plotly.express as px
df = px.data.medals_long()
fig = px.scatter(df, y="count", x="nation", color="medal")
fig.update_traces(marker_size=10)
fig.update_layout(scattermode="group", scattergap=0.75)
fig.show()
#Error Bars
import plotly.express as px
df = px.data.iris()
df["e"] = df["sepal_width"]/100
fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species",
error_x="e", error_y="e")
fig.show()
#Marginal Distribution Plots
import plotly.express as px
df = px.data.iris()
fig = px.scatter(df, x="sepal_length", y="sepal_width", marginal_x="histogram", marginal_y="rug")
fig.show()
#Facetting
import plotly.express as px
df = px.data.tips()
fig = px.scatter(df, x="total_bill", y="tip", color="smoker", facet_col="sex", facet_row="time")
fig.show()
#Facet and Trellis Plots in Python
#Scatter Plot Column Facets
import plotly.express as px
df = px.data.tips()
fig = px.scatter(df, x="total_bill", y="tip", color="smoker", facet_col="sex")
fig.show()
#Bar Chart Row Facets
import plotly.express as px
df = px.data.tips()
fig = px.bar(df, x="size", y="total_bill", color="sex", facet_row="smoker")
fig.show()
#Wrapping Column Facets
import plotly.express as px
df = px.data.gapminder()
fig = px.scatter(df, x='gdpPercap', y='lifeExp', color='continent', size='pop',
facet_col='year', facet_col_wrap=4)
fig.show()
#Histogram Facet Grids
import plotly.express as px
df = px.data.tips()
fig = px.histogram(df, x="total_bill", y="tip", color="sex", facet_row="time", facet_col="day",
category_orders={"day": ["Thur", "Fri", "Sat", "Sun"], "time": ["Lunch", "Dinner"]})
fig.show()
#Choropleth Column Facets
import plotly.express as px
df = px.data.election()
df = df.melt(id_vars="district", value_vars=["Coderre", "Bergeron", "Joly"],
var_name="candidate", value_name="votes")
geojson = px.data.election_geojson()
fig = px.choropleth(df, geojson=geojson, color="votes", facet_col="candidate",
locations="district", featureidkey="properties.district",
projection="mercator"
)
fig.update_geos(fitbounds="locations", visible=False)
fig.show()
#Adding Lines and Rectangles to Facet Plots
import plotly.express as px
df = px.data.stocks(indexed=True)
fig = px.line(df, facet_col="company", facet_col_wrap=2)
fig.add_hline(y=1, line_dash="dot",
annotation_text="Jan 1, 2018 baseline",
annotation_position="bottom right")
fig.add_vrect(x0="2018-09-24", x1="2018-12-18", col=1,
annotation_text="decline", annotation_position="top left",
fillcolor="green", opacity=0.25, line_width=0)
fig.show()
#Adding the Same Trace to All Facets
import plotly.express as px
df = px.data.tips()
fig = px.scatter(df, x="total_bill", y="tip", color='sex',
facet_col="day", facet_row="time")
import statsmodels.api as sm
import plotly.graph_objects as go
df = df.sort_values(by="total_bill")
model = sm.OLS(df["tip"], sm.add_constant(df["total_bill"])).fit()
#create the trace to be added to all facets
trace = go.Scatter(x=df["total_bill"], y=model.predict(),
line_color="black", name="overall OLS")
# give it a legend group and hide it from the legend
trace.update(legendgroup="trendline", showlegend=False)
# add it to all rows/cols, but not to empty subplots
fig.add_trace(trace, row="all", col="all", exclude_empty_subplots=True)
# set only the last trace added to appear in the legend
# `selector=-1` introduced in plotly v4.13
fig.update_traces(selector=-1, showlegend=True)
fig.show()
#Facets With Independent Axes
#By default, facet axes are linked together: zooming inside one of the
#facets will also zoom in the other facets. You can disable this behaviour when
#you use facet_row only, by disabling matches on the Y axes, or when using facet_col only,
#by disabling matches on the X axes. It is not recommended to use this approach when using facet_row and
#facet_col together, as in this case it
#becomes very hard to understand the labelling of axes and grid lines.
import plotly.express as px
df = px.data.tips()
fig = px.scatter(df, x="total_bill", y="tip", color='sex', facet_row="day")
fig.update_yaxes(matches=None)
fig.show()
import plotly.express as px
df = px.data.tips()
fig = px.scatter(df, x="total_bill", y="tip", color='sex', facet_col="day")
fig.update_xaxes(matches=None)
fig.show()
#Customizing Subplot Figure Titles
#In the following example, we pass a lambda function to for_each_annotation in order to change the figure subplot
#titles from smoker=No and smoker=Yes to just No and Yes.
import plotly.express as px
fig = px.scatter(px.data.tips(), x="total_bill", y="tip", facet_col="smoker")
fig.for_each_annotation(lambda a: a.update(text=a.text.split("=")[-1]))
fig.show()
#Controlling Facet Ordering
import plotly.express as px
df = px.data.tips()
fig = px.bar(df, x="day", y="total_bill", color="smoker", barmode="group", facet_col="sex",
category_orders={"day": ["Thur", "Fri", "Sat", "Sun"],
"smoker": ["Yes", "No"],
"sex": ["Male", "Female"]})
fig.show()
#Controlling Facet Spacing
#The facet_row_spacing and facet_col_spacing arguments can be used to control the spacing between rows and columns. These values are specified in fractions of the plotting area in paper coordinates and not in pixels, so they will grow or shrink with the width and height of the figure.
#The defaults work well with 1-4 rows or columns at the default figure size with the default font size, but need to be reduced to around 0.01 for very large figures or figures with many rows or columns. Conversely, if activating tick labels on all facets, the spacing will need to be increased.
import plotly.express as px
df = px.data.gapminder().query("continent == 'Africa'")
fig = px.line(df, x="year", y="lifeExp", facet_col="country", facet_col_wrap=7,
facet_row_spacing=0.04, # default is 0.07 when facet_col_wrap is used
facet_col_spacing=0.04, # default is 0.03
height=600, width=800,
title="Life Expectancy in Africa")
fig.for_each_annotation(lambda a: a.update(text=a.text.split("=")[-1]))
fig.update_yaxes(showticklabels=True)
fig.show()
#Synchronizing axes in subplots with matches
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import numpy as np
N = 20
x = np.linspace(0, 1, N)
fig = make_subplots(1, 3)
for i in range(1, 4):
fig.add_trace(go.Scatter(x=x, y=np.random.random(N)), 1, i)
fig.update_xaxes(matches='x')
fig.show()
#Horizontal and Vertical Lines and Rectangles
import plotly.express as px
df = px.data.iris()
fig = px.scatter(df, x="petal_length", y="petal_width")
fig.add_hline(y=0.9)
fig.add_vrect(x0=0.9, x1=2)
fig.show()
import plotly.express as px
df = px.data.iris()
fig = px.scatter(df, x="petal_length", y="petal_width")
fig.add_vline(x=2.5, line_width=3, line_dash="dash", line_color="green")
fig.add_hrect(y0=0.9, y1=2.6, line_width=0, fillcolor="red", opacity=0.2)
fig.show()
#Adding Text Annotations
import plotly.express as px
df = px.data.stocks(indexed=True)
fig = px.line(df)
fig.add_hline(y=1, line_dash="dot",
annotation_text="Jan 1, 2018 baseline",
annotation_position="bottom right")
fig.add_vrect(x0="2018-09-24", x1="2018-12-18",
annotation_text="decline", annotation_position="top left",
fillcolor="green", opacity=0.25, line_width=0)
fig.show()
#Extra formatting of the annotation can be done using magic-underscores prefixed by annotation_ or by passing a dict or go.layout.Annotation instance to the annotation argument:
import plotly.express as px
df = px.data.stocks(indexed=True)
fig = px.line(df)
fig.add_hline(y=1, line_dash="dot",
annotation_text="Jan 1, 2018 baseline",
annotation_position="bottom right",
annotation_font_size=20,
annotation_font_color="blue"
)
fig.add_vrect(x0="2018-09-24", x1="2018-12-18",
annotation_text="decline", annotation_position="top left",
annotation=dict(font_size=20, font_family="Times New Roman"),
fillcolor="green", opacity=0.25, line_width=0)
fig.show()
#Adding to Multiple Facets / Subplots
import plotly.express as px
df = px.data.stocks(indexed=True)
fig = px.line(df, facet_col="company", facet_col_wrap=2)
fig.add_hline(y=1, line_dash="dot", row=3, col="all",
annotation_text="Jan 1, 2018 baseline",
annotation_position="bottom right")
fig.add_vrect(x0="2018-09-24", x1="2018-12-18", row="all", col=1,
annotation_text="decline", annotation_position="top left",
fillcolor="green", opacity=0.25, line_width=0)
fig.show()
#Text on scatter plots with Plotly Express
import plotly.express as px
df = px.data.gapminder().query("year==2007 and continent=='Americas'")
fig = px.scatter(df, x="gdpPercap", y="lifeExp", text="country", log_x=True, size_max=60)
fig.update_traces(textposition='top center')
fig.update_layout(
height=800,
title_text='GDP and Life Expectancy (Americas, 2007)'
)
fig.show()
#Text on scatter plots with Graph Objects
import plotly.graph_objects as go
fig = go.Figure()
fig.add_trace(go.Scatter(
x=[0, 1, 2],
y=[1, 1, 1],
mode="lines+markers+text",
name="Lines, Markers and Text",
text=["Text A", "Text B", "Text C"],
textposition="top center"
))
fig.add_trace(go.Scatter(
x=[0, 1, 2],
y=[2, 2, 2],
mode="markers+text",
name="Markers and Text",
text=["Text D", "Text E", "Text F"],
textposition="bottom center"
))
fig.add_trace(go.Scatter(
x=[0, 1, 2],
y=[3, 3, 3],
mode="lines+text",
name="Lines and Text",
text=["Text G", "Text H", "Text I"],
textposition="bottom center"
))
fig.show()
#Controlling Text Size with uniformtext
import plotly.express as px
df = px.data.gapminder(year=2007)
fig = px.bar(df, x='continent', y='pop', color="lifeExp", text='country',
title="Default behavior: some text is tiny")
fig.update_traces(textposition='inside')
fig.show()
#Here is the same figure with uniform text applied: the text for all bars is the same size, with a minimum size of 8. Any text at the minimum size which does not fit in the bar is hidden.
import plotly.express as px
df = px.data.gapminder(year=2007)
fig = px.bar(df, x='continent', y='pop', color="lifeExp", text='country',
title="Uniform Text: min size is 8, hidden if can't fit")
fig.update_traces(textposition='inside')
fig.update_layout(uniformtext_minsize=8, uniformtext_mode='hide')
fig.show()
import plotly.express as px
df = px.data.gapminder().query("continent == 'Asia' and year == 2007")
fig = px.pie(df, values='pop', names='country')
fig.update_traces(textposition='inside')
fig.update_layout(uniformtext_minsize=12, uniformtext_mode='hide')
fig.show()
import plotly.express as px
df = px.data.gapminder().query("continent == 'Asia' and year == 2007")
fig = px.pie(df, values='pop', names='country')
fig.update_traces(textposition='inside', textfont_size=14)
fig.show()
#Text Annotations
import plotly.graph_objects as go
fig = go.Figure()
fig.add_trace(go.Scatter(
x=[0, 1, 2, 3, 4, 5, 6, 7, 8],
y=[0, 1, 3, 2, 4, 3, 4, 6, 5]
))
fig.add_trace(go.Scatter(
x=[0, 1, 2, 3, 4, 5, 6, 7, 8],
y=[0, 4, 5, 1, 2, 2, 3, 4, 2]
))
fig.add_annotation(x=2, y=5,
text="Text annotation with arrow",
showarrow=True,
arrowhead=1)
fig.add_annotation(x=4, y=4,
text="Text annotation without arrow",
showarrow=False,
yshift=10)
fig.update_layout(showlegend=False)
fig.show()
#3D Annotations
import plotly.graph_objects as go
fig = go.Figure()
fig.add_trace(go.Scatter3d(
x=["2017-01-01", "2017-02-10", "2017-03-20"],
y=["A", "B", "C"],
z=[1, 1000, 100000],
name="z",
))
fig.update_layout(
scene=dict(
xaxis=dict(type="date"),
yaxis=dict(type="category"),
zaxis=dict(type="log"),
annotations=[
dict(
showarrow=False,
x="2017-01-01",
y="A",
z=0,
text="Point 1",
xanchor="left",
xshift=10,
opacity=0.7),
dict(
x="2017-02-10",
y="B",
z=4,
text="Point 2",
textangle=0,
ax=0,
ay=-75,
font=dict(
color="black",
size=12
),
arrowcolor="black",
arrowsize=3,
arrowwidth=1,
arrowhead=1),
dict(
x="2017-03-20",
y="C",
z=5,
ax=50,
ay=0,
text="Point 3",
arrowhead=1,
xanchor="left",
yanchor="bottom"
)]
),
)
fig.show()
#Custom Text Color and Styling
import plotly.graph_objects as go
fig = go.Figure()
fig.add_trace(go.Scatter(
x=[0, 1, 2],
y=[1, 1, 1],
mode="lines+markers+text",
name="Lines, Markers and Text",
text=["Text A", "Text B", "Text C"],
textposition="top right",
textfont=dict(
family="sans serif",
size=18,
color="crimson"
)
))
fig.add_trace(go.Scatter(
x=[0, 1, 2],
y=[2, 2, 2],
mode="lines+markers+text",
name="Lines and Text",
text=["Text G", "Text H", "Text I"],
textposition="bottom center",
textfont=dict(
family="sans serif",
size=18,
color="LightSeaGreen"
)
))
fig.update_layout(showlegend=False)
fig.show()
#Styling and Coloring Annotations
import plotly.graph_objects as go
fig = go.Figure()
fig.add_trace(go.Scatter(
x=[0, 1, 2, 3, 4, 5, 6, 7, 8],
y=[0, 1, 3, 2, 4, 3, 4, 6, 5]
))
fig.add_trace(go.Scatter(
x=[0, 1, 2, 3, 4, 5, 6, 7, 8],
y=[0, 4, 5, 1, 2, 2, 3, 4, 2]
))
fig.add_annotation(
x=2,
y=5,
xref="x",
yref="y",
text="max=5",
showarrow=True,
font=dict(
family="Courier New, monospace",
size=16,
color="#ffffff"
),
align="center",
arrowhead=2,
arrowsize=1,
arrowwidth=2,
arrowcolor="#636363",
ax=20,
ay=-30,
bordercolor="#c7c7c7",
borderwidth=2,
borderpad=4,
bgcolor="#ff7f0e",
opacity=0.8
)
fig.update_layout(showlegend=False)
fig.show()
#Text Font as an Array - Styling Each Text Element
import plotly.graph_objects as go
fig = go.Figure()
fig.add_trace(go.Scattergeo(
lat=[45.5, 43.4, 49.13, 51.1, 53.34, 45.24, 44.64, 48.25, 49.89, 50.45],
lon=[-73.57, -79.24, -123.06, -114.1, -113.28, -75.43, -63.57, -123.21, -97.13,
-104.6],
marker={
"color": ["MidnightBlue", "IndianRed", "MediumPurple", "Orange", "Crimson",
"LightSeaGreen", "RoyalBlue", "LightSalmon", "DarkOrange", "MediumSlateBlue"],
"line": {
"width": 1
},
"size": 10
},
mode="markers+text",
name="",
text=["Montreal", "Toronto", "Vancouver", "Calgary", "Edmonton", "Ottawa",
"Halifax",
"Victoria", "Winnepeg", "Regina"],
textfont={
"color": ["MidnightBlue", "IndianRed", "MediumPurple", "Gold", "Crimson",
"LightSeaGreen",
"RoyalBlue", "LightSalmon", "DarkOrange", "MediumSlateBlue"],
"family": ["Arial, sans-serif", "Balto, sans-serif", "Courier New, monospace",
"Droid Sans, sans-serif", "Droid Serif, serif",
"Droid Sans Mono, sans-serif",
"Gravitas One, cursive", "Old Standard TT, serif",
"Open Sans, sans-serif",
"PT Sans Narrow, sans-serif", "Raleway, sans-serif",
"Times New Roman, Times, serif"],
"size": [22, 21, 20, 19, 18, 17, 16, 15, 14, 13]
},
textposition=["top center", "middle left", "top center", "bottom center",
"top right",
"middle left", "bottom right", "bottom left", "top right",
"top right"]
))
fig.update_layout(
title_text="Canadian cities",
geo=dict(
lataxis=dict(range=[40, 70]),
lonaxis=dict(range=[-130, -55]),
scope="north america"
)
)
fig.show()
#Positioning Text Annotations Absolutely
import plotly.express as px
fig = px.scatter(x=[1, 2, 3], y=[1, 2, 3], title="Try panning or zooming!")
fig.add_annotation(text="Absolutely-positioned annotation",
xref="paper", yref="paper",
x=0.3, y=0.3, showarrow=False)
fig.show()
#Adding Annotations Referenced to an Axis
import plotly.express as px
import plotly.graph_objects as go
df = px.data.wind()
fig = px.scatter(df, y="frequency")
# Set a custom domain to see how the ' domain' string changes the behaviour
fig.update_layout(xaxis=dict(domain=[0, 0.5]), yaxis=dict(domain=[0.25, 0.75]))
fig.add_annotation(
xref="x domain",
yref="y domain",
# The arrow head will be 25% along the x axis, starting from the left
x=0.25,
# The arrow head will be 40% along the y axis, starting from the bottom
y=0.4,
text="An annotation referencing the axes",
arrowhead=2,
)
fig.show()
import plotly.graph_objects as go
fig = go.Figure(go.Pie(
values = [40000000, 20000000, 30000000, 10000000],
labels = ["Wages", "Operating expenses", "Cost of sales", "Insurance"],
texttemplate = "%{label}: %{value:$,s} <br>(%{percent})",
textposition = "inside"))
fig.show()
#Set Date in Text Template
from plotly import graph_objects as go
fig = go.Figure()
fig.add_trace(go.Funnel(
name = 'Montreal',
orientation = "h",
y = ["2018-01-01", "2018-07-01", "2019-01-01", "2020-01-01"],
x = [100, 60, 40, 20],
textposition = "inside",
texttemplate = "%{y| %a. %_d %b %Y}"))
fig.add_trace(go.Funnel(
name = 'Vancouver',
orientation = "h",
y = ["2018-01-01", "2018-07-01", "2019-01-01", "2020-01-01"],
x = [90, 70, 50, 10],
textposition = "inside",
textinfo = "label"))
fig.update_layout(yaxis = {'type': 'date'})
fig.show()
#Pie Charts in Python
#Pie chart with plotly express
import plotly.express as px
df = px.data.gapminder().query("year == 2007").query("continent == 'Europe'")
df.loc[df['pop'] < 2.e6, 'country'] = 'Other countries' # Represent only large countries
fig = px.pie(df, values='pop', names='country', title='Population of European continent')
fig.show()
#Pie chart with repeated labels
import plotly.express as px
# This dataframe has 244 lines, but 4 distinct values for `day`
df = px.data.tips()
fig = px.pie(df, values='tip', names='day')
fig.show()
#Setting the color of pie sectors with px.pie
import plotly.express as px
df = px.data.tips()
fig = px.pie(df, values='tip', names='day', color_discrete_sequence=px.colors.sequential.RdBu)
fig.show()
#Using an explicit mapping for discrete colors
import plotly.express as px
df = px.data.tips()
fig = px.pie(df, values='tip', names='day', color='day',
color_discrete_map={'Thur':'lightcyan',
'Fri':'cyan',
'Sat':'royalblue',
'Sun':'darkblue'})
fig.show()
#Customizing a pie chart created with px.pie
#we first create a pie chart with px,pie, using some of its options such as hover_data (which columns should appear in the hover) or labels (renaming column names). For further tuning, we call fig.update_traces to set other parameters of the chart
#(you can also use fig.update_layout for changing the layout).
import plotly.express as px
df = px.data.gapminder().query("year == 2007").query("continent == 'Americas'")
fig = px.pie(df, values='pop', names='country',
title='Population of American continent',
hover_data=['lifeExp'], labels={'lifeExp':'life expectancy'})
fig.update_traces(textposition='inside', textinfo='percent+label')
fig.show()
#Basic Pie Chart with go.Pie
import plotly.graph_objects as go
labels = ['Oxygen','Hydrogen','Carbon_Dioxide','Nitrogen']
values = [4500, 2500, 1053, 500]
fig = go.Figure(data=[go.Pie(labels=labels, values=values)])
fig.show()
#Styled Pie Chart
import plotly.graph_objects as go
colors = ['gold', 'mediumturquoise', 'darkorange', 'lightgreen']
fig = go.Figure(data=[go.Pie(labels=['Oxygen','Hydrogen','Carbon_Dioxide','Nitrogen'],
values=[4500,2500,1053,500])])
fig.update_traces(hoverinfo='label+percent', textinfo='value', textfont_size=20,
marker=dict(colors=colors, line=dict(color='#000000', width=2)))
fig.show()
#Controlling text fontsize with uniformtext
#If you want all the text labels to have the same size, you can use the uniformtext layout parameter.
#The minsize attribute sets the font size, and the mode attribute sets what happens for labels which cannot fit
#with the desired fontsize: either hide them or show them with overflow. In the example below we also force the
#text to be inside with textposition,
#otherwise text labels which do not fit are displayed outside of pie sectors.
import plotly.express as px
df = px.data.gapminder().query("continent == 'Asia'")
fig = px.pie(df, values='pop', names='country')
fig.update_traces(textposition='inside')
fig.update_layout(uniformtext_minsize=12, uniformtext_mode='hide')
fig.show()
#Controlling text orientation inside pie sectors
#The insidetextorientation attribute controls the orientation of text inside sectors. With "auto" the texts may automatically be rotated to fit with the maximum size inside the slice. Using "horizontal" (resp. "radial", "tangential")
#forces text to be horizontal (resp. radial or tangential)
#For a figure fig created with plotly express, use fig.update_traces(insidetextorientation='...') to change the text orientation.
import plotly.graph_objects as go
labels = ['Oxygen','Hydrogen','Carbon_Dioxide','Nitrogen']
values = [4500, 2500, 1053, 500]
fig = go.Figure(data=[go.Pie(labels=labels, values=values, textinfo='label+percent',
insidetextorientation='radial'
)])
fig.show()
#Donut Chart
import plotly.graph_objects as go
labels = ['Oxygen','Hydrogen','Carbon_Dioxide','Nitrogen']
values = [4500, 2500, 1053, 500]
# Use `hole` to create a donut-like pie chart
fig = go.Figure(data=[go.Pie(labels=labels, values=values, hole=.3)])
fig.show()
#Pulling sectors out from the center
#For a "pulled-out" or "exploded" layout of the pie chart, use the pull argument. It can be a scalar for pulling all sectors or an array to pull only some of the sectors.
import plotly.graph_objects as go
labels = ['Oxygen','Hydrogen','Carbon_Dioxide','Nitrogen']
values = [4500, 2500, 1053, 500]
# pull is given as a fraction of the pie radius
fig = go.Figure(data=[go.Pie(labels=labels, values=values, pull=[0, 0, 0.2, 0])])
fig.show()
#Pie Charts in subplots
import plotly.graph_objects as go
from plotly.subplots import make_subplots
labels = ["US", "China", "European Union", "Russian Federation", "Brazil", "India",
"Rest of World"]
# Create subplots: use 'domain' type for Pie subplot
fig = make_subplots(rows=1, cols=2, specs=[[{'type':'domain'}, {'type':'domain'}]])
fig.add_trace(go.Pie(labels=labels, values=[16, 15, 12, 6, 5, 4, 42], name="GHG Emissions"),
1, 1)
fig.add_trace(go.Pie(labels=labels, values=[27, 11, 25, 8, 1, 3, 25], name="CO2 Emissions"),
1, 2)
# Use `hole` to create a donut-like pie chart
fig.update_traces(hole=.4, hoverinfo="label+percent+name")
fig.update_layout(
title_text="Global Emissions 1990-2011",
# Add annotations in the center of the donut pies.
annotations=[dict(text='GHG', x=0.18, y=0.5, font_size=20, showarrow=False),
dict(text='CO2', x=0.82, y=0.5, font_size=20, showarrow=False)])
fig.show()
import plotly.graph_objects as go
from plotly.subplots import make_subplots
labels = ['1st', '2nd', '3rd', '4th', '5th']
# Define color sets of paintings
night_colors = ['rgb(56, 75, 126)', 'rgb(18, 36, 37)', 'rgb(34, 53, 101)',
'rgb(36, 55, 57)', 'rgb(6, 4, 4)']
sunflowers_colors = ['rgb(177, 127, 38)', 'rgb(205, 152, 36)', 'rgb(99, 79, 37)',
'rgb(129, 180, 179)', 'rgb(124, 103, 37)']
irises_colors = ['rgb(33, 75, 99)', 'rgb(79, 129, 102)', 'rgb(151, 179, 100)',
'rgb(175, 49, 35)', 'rgb(36, 73, 147)']
cafe_colors = ['rgb(146, 123, 21)', 'rgb(177, 180, 34)', 'rgb(206, 206, 40)',
'rgb(175, 51, 21)', 'rgb(35, 36, 21)']
# Create subplots, using 'domain' type for pie charts
specs = [[{'type':'domain'}, {'type':'domain'}], [{'type':'domain'}, {'type':'domain'}]]
fig = make_subplots(rows=2, cols=2, specs=specs)
# Define pie charts
fig.add_trace(go.Pie(labels=labels, values=[38, 27, 18, 10, 7], name='Starry Night',
marker_colors=night_colors), 1, 1)
fig.add_trace(go.Pie(labels=labels, values=[28, 26, 21, 15, 10], name='Sunflowers',
marker_colors=sunflowers_colors), 1, 2)
fig.add_trace(go.Pie(labels=labels, values=[38, 19, 16, 14, 13], name='Irises',
marker_colors=irises_colors), 2, 1)
fig.add_trace(go.Pie(labels=labels, values=[31, 24, 19, 18, 8], name='The Night Café',
marker_colors=cafe_colors), 2, 2)
# Tune layout and hover info
fig.update_traces(hoverinfo='label+percent+name', textinfo='none')
fig.update(layout_title_text='Van Gogh: 5 Most Prominent Colors Shown Proportionally',
layout_showlegend=False)
fig = go.Figure(fig)
fig.show()
#Plot chart with area proportional to total count
#Plots in the same scalegroup are represented with an area proportional to their total size.
import plotly.graph_objects as go
from plotly.subplots import make_subplots
labels = ["Asia", "Europe", "Africa", "Americas", "Oceania"]
fig = make_subplots(1, 2, specs=[[{'type':'domain'}, {'type':'domain'}]],
subplot_titles=['1980', '2007'])
fig.add_trace(go.Pie(labels=labels, values=[4, 7, 1, 7, 0.5], scalegroup='one',
name="World GDP 1980"), 1, 1)
fig.add_trace(go.Pie(labels=labels, values=[21, 15, 3, 19, 1], scalegroup='one',
name="World GDP 2007"), 1, 2)
fig.update_layout(title_text='World GDP')
fig.show()
#Sunburst charts
import plotly.graph_objects as go
fig =go.Figure(go.Sunburst(
labels=["Eve", "Cain", "Seth", "Enos", "Noam", "Abel", "Awan", "Enoch", "Azura"],
parents=["", "Eve", "Eve", "Seth", "Seth", "Eve", "Eve", "Awan", "Eve" ],
values=[10, 14, 12, 10, 2, 6, 6, 4, 4],
))
fig.update_layout(margin = dict(t=0, l=0, r=0, b=0))
fig.show()
#Linear Regression and Other Trendlines
import plotly.express as px
df = px.data.tips()
fig = px.scatter(df, x="total_bill", y="tip", trendline="ols")
fig.show()
#Displaying a single trendline with multiple traces
#To display a single trendline using the entire dataset, set the trendline_scope argument to "overall".
#The same trendline will be overlaid on all facets and animation frames.
#The trendline color can be overridden with trendline_color_override.
import plotly.express as px
df = px.data.tips()
fig = px.scatter(df, x="total_bill", y="tip", symbol="smoker", color="sex", trendline="ols", trendline_scope="overall")
fig.show()
import plotly.express as px
df = px.data.tips()
fig = px.scatter(df, x="total_bill", y="tip", facet_col="smoker", color="sex",
trendline="ols", trendline_scope="overall", trendline_color_override="black")
fig.show()
#OLS Parameters
#OLS trendlines can be fit with log transformations to both X or Y data using the trendline_options argument, independently of whether or not the plot has logarithmic axes.
import plotly.express as px
df = px.data.gapminder(year=2007)
fig = px.scatter(df, x="gdpPercap", y="lifeExp",
trendline="ols", trendline_options=dict(log_x=True),
title="Log-transformed fit on linear axes")
fig.show()
import plotly.express as px
df = px.data.gapminder(year=2007)
fig = px.scatter(df, x="gdpPercap", y="lifeExp", log_x=True,
trendline="ols", trendline_options=dict(log_x=True),
title="Log-scaled X axis and log-transformed fit")
fig.show()
#Locally WEighted Scatterplot Smoothing (LOWESS)
import plotly.express as px
df = px.data.stocks(datetimes=True)
fig = px.scatter(df, x="date", y="GOOG", trendline="lowess")
fig.show()
#The level of smoothing can be controlled via the frac trendline option, which indicates the fraction of the data that the LOWESS smoother should include. The default is a fairly smooth line with frac=0.6666 and lowering this fraction will give a line that more closely follows the data.
import plotly.express as px
df = px.data.stocks(datetimes=True)
fig = px.scatter(df, x="date", y="GOOG", trendline="lowess", trendline_options=dict(frac=0.1))
fig.show()
#Moving Averages 5 point
import plotly.express as px
df = px.data.stocks(datetimes=True)
fig = px.scatter(df, x="date", y="GOOG", trendline="rolling", trendline_options=dict(window=5),
title="5-point moving average")
fig.show()
import plotly.express as px
df = px.data.stocks(datetimes=True)
fig = px.scatter(df, x="date", y="GOOG", trendline="ewm", trendline_options=dict(halflife=2),
title="Exponentially-weighted moving average (halflife of 2 points)")
fig.show()
import plotly.express as px
df = px.data.stocks(datetimes=True)
fig = px.scatter(df, x="date", y="GOOG", trendline="expanding", title="Expanding mean")
fig.show()
#Other Functions
#The rolling, expanding and ewm trendlines support other functions than the default mean, enabling, for example, a moving-median trendline, or an expanding-max trendline.
import plotly.express as px
df = px.data.stocks(datetimes=True)
fig = px.scatter(df, x="date", y="GOOG", trendline="rolling", trendline_options=dict(function="median", window=5),
title="Rolling Median")
fig.show()
import plotly.express as px
df = px.data.stocks(datetimes=True)
fig = px.scatter(df, x="date", y="GOOG", trendline="expanding", trendline_options=dict(function="max"),
title="Expanding Maximum")
fig.show()
import plotly.express as px
df = px.data.stocks(datetimes=True)
fig = px.scatter(df, x="date", y="GOOG", trendline="rolling",
trendline_options=dict(window=5, win_type="gaussian", function_args=dict(std=2)),
title="Rolling Mean with Gaussian Window")
fig.show()
#Displaying only the trendlines
import plotly.express as px
df = px.data.stocks(indexed=True, datetimes=True)
fig = px.scatter(df, trendline="rolling", trendline_options=dict(window=5),
title="5-point moving average")
fig.data = [t for t in fig.data if t.mode == "lines"]
fig.update_traces(showlegend=True) #trendlines have showlegend=False by default
fig.show()
#Log Plots in Python
#Logarithmic Axes with Plotly Express
import plotly.express as px
df = px.data.gapminder().query("year == 2007")
fig = px.scatter(df, x="gdpPercap", y="lifeExp", hover_name="country", log_x=True)
fig.show()
#Setting the range of a logarithmic axis with Plotly Express works the same was as with linear axes: using the range_x and range_y keywords. Note that you cannot set the range to include 0 or less.
import plotly.express as px
df = px.data.gapminder().query("year == 2007")
fig = px.scatter(df, x="gdpPercap", y="lifeExp", hover_name="country",
log_x=True, range_x=[1,100000], range_y=[0,100])
fig.show()
#Adding minor ticks
import plotly.express as px
df = px.data.gapminder().query("year == 2007")
fig = px.scatter(df, x="gdpPercap", y="lifeExp", hover_name="country",
log_x=True, range_x=[1,100000], range_y=[0,100])
fig.update_xaxes(minor=dict(ticks="inside", ticklen=6, showgrid=True))
fig.show()
#Logarithmic Axes with Graph Objects
import plotly.graph_objects as go
import plotly.express as px
df = px.data.gapminder().query("year == 2007")
fig = go.Figure()
fig.add_trace(go.Scatter(mode="markers", x=df["gdpPercap"], y=df["lifeExp"] ))
fig.update_xaxes(type="log")
fig.show()
#Setting the range of a logarithmic axis with plotly.graph_objects is very different than setting the range of linear axes: the range is set using the exponent rather than the actual value:
import plotly.graph_objects as go
import plotly.express as px
df = px.data.gapminder().query("year == 2007")
fig = go.Figure()
fig.add_trace(go.Scatter(mode="markers", x=df["gdpPercap"], y=df["lifeExp"] ))
fig.update_xaxes(type="log", range=[0,5]) # log range: 10^0=1, 10^5=100000
fig.update_yaxes(range=[0,100]) # linear range
fig.show()
#Comparing Graph Objects and Plotly Express
import pandas as pd
df = pd.DataFrame({
"Fruit": ["Apples", "Oranges", "Bananas", "Apples", "Oranges", "Bananas"],
"Contestant": ["Alex", "Alex", "Alex", "Jordan", "Jordan", "Jordan"],
"Number Eaten": [2, 1, 3, 1, 3, 2],
})
# Plotly Express
import plotly.express as px
fig = px.bar(df, x="Fruit", y="Number Eaten", color="Contestant", barmode="group")
fig.show()
# Graph Objects
import plotly.graph_objects as go
fig = go.Figure()
for contestant, group in df.groupby("Contestant"):
fig.add_trace(go.Bar(x=group["Fruit"], y=group["Number Eaten"], name=contestant,
hovertemplate="Contestant=%s<br>Fruit=%%{x}<br>Number Eaten=%%{y}<extra></extra>"% contestant))
fig.update_layout(legend_title_text = "Contestant")
fig.update_xaxes(title_text="Fruit")
fig.update_yaxes(title_text="Number Eaten")
fig.show()
#Sunburst of a rectangular DataFrame with plotly.express
#Hierarchical data are often stored as a rectangular dataframe, with different columns corresponding to different levels of the hierarchy. px.sunburst can take a path parameter corresponding to a list of columns. Note that id and parent should not be provided if path is given.
import plotly.express as px
df = px.data.tips()
fig = px.sunburst(df, path=['day', 'time', 'sex'], values='total_bill')
fig.show()
#Sunburst of a rectangular DataFrame with continuous color argument in px.sunburst
import plotly.express as px
import numpy as np
df = px.data.gapminder().query("year == 2007")
fig = px.sunburst(df, path=['continent', 'country'], values='pop',
color='lifeExp', hover_data=['iso_alpha'],
color_continuous_scale='RdBu',
color_continuous_midpoint=np.average(df['lifeExp'], weights=df['pop']))
fig.show()
#Sunburst of a rectangular DataFrame with discrete color argument in px.sunburst
#When the argument of color corresponds to non-numerical data, discrete colors are used. If a sector has the same value of the color column for all its children, then the corresponding color is used, otherwise the first color of the discrete color sequence is used.
import plotly.express as px
df = px.data.tips()
fig = px.sunburst(df, path=['sex', 'day', 'time'], values='total_bill', color='day')
fig.show()
import plotly.express as px
df = px.data.tips()
fig = px.sunburst(df, path=['sex', 'day', 'time'], values='total_bill', color='time')
fig.show()
#Using an explicit mapping for discrete colors
import plotly.express as px
df = px.data.tips()
fig = px.sunburst(df, path=['sex', 'day', 'time'], values='total_bill', color='time',
color_discrete_map={'(?)':'black', 'Lunch':'gold', 'Dinner':'darkblue'})
fig.show()
#Rectangular data with missing values
import plotly.express as px
import pandas as pd
vendors = ["A", "B", "C", "D", None, "E", "F", "G", "H", None]
sectors = ["Tech", "Tech", "Finance", "Finance", "Other",
"Tech", "Tech", "Finance", "Finance", "Other"]
regions = ["North", "North", "North", "North", "North",
"South", "South", "South", "South", "South"]
sales = [1, 3, 2, 4, 1, 2, 2, 1, 4, 1]
df = pd.DataFrame(
dict(vendors=vendors, sectors=sectors, regions=regions, sales=sales)
)
print(df)
fig = px.sunburst(df, path=['regions', 'sectors', 'vendors'], values='sales')
fig.show()
vendors sectors regions sales 0 A Tech North 1 1 B Tech North 3 2 C Finance North 2 3 D Finance North 4 4 None Other North 1 5 E Tech South 2 6 F Tech South 2 7 G Finance South 1 8 H Finance South 4 9 None Other South 1
#Sunburst with Repeated Labels
import plotly.graph_objects as go
fig =go.Figure(go.Sunburst(
ids=[
"North America", "Europe", "Australia", "North America - Football", "Soccer",
"North America - Rugby", "Europe - Football", "Rugby",
"Europe - American Football","Australia - Football", "Association",
"Australian Rules", "Autstralia - American Football", "Australia - Rugby",
"Rugby League", "Rugby Union"
],
labels= [
"North<br>America", "Europe", "Australia", "Football", "Soccer", "Rugby",
"Football", "Rugby", "American<br>Football", "Football", "Association",
"Australian<br>Rules", "American<br>Football", "Rugby", "Rugby<br>League",
"Rugby<br>Union"
],
parents=[
"", "", "", "North America", "North America", "North America", "Europe",
"Europe", "Europe","Australia", "Australia - Football", "Australia - Football",
"Australia - Football", "Australia - Football", "Australia - Rugby",
"Australia - Rugby"
],
))
fig.update_layout(margin = dict(t=0, l=0, r=0, b=0))
fig.show()
#Branchvalues
#With branchvalues "total", the value of the parent represents the width of its wedge. In the example below, "Enoch" is 4 and "Awan" is 6 and so Enoch's width is 4/6ths of Awans. With branchvalues "remainder", the parent's width is determined by its own value plus those of its children. So, Enoch's width is 4/10ths of Awan's (4 / (6 + 4)).
#Note that this means that the sum of the values of the children cannot exceed the value of their parent when branchvalues is set to "total". When branchvalues is set to "remainder" (the default), children will not take up all of the space below their parent (unless the parent is the root and it has a value of 0).
import plotly.graph_objects as go
fig =go.Figure(go.Sunburst(
labels=[ "Eve", "Cain", "Seth", "Enos", "Noam", "Abel", "Awan", "Enoch", "Azura"],
parents=["", "Eve", "Eve", "Seth", "Seth", "Eve", "Eve", "Awan", "Eve" ],
values=[ 65, 14, 12, 10, 2, 6, 6, 4, 4],
branchvalues="total",
))
fig.update_layout(margin = dict(t=0, l=0, r=0, b=0))
fig.show()
#Large Number of Slices
import plotly.graph_objects as go
import pandas as pd
df1 = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/718417069ead87650b90472464c7565dc8c2cb1c/sunburst-coffee-flavors-complete.csv')
df2 = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/718417069ead87650b90472464c7565dc8c2cb1c/coffee-flavors.csv')
fig = go.Figure()
fig.add_trace(go.Sunburst(
ids=df1.ids,
labels=df1.labels,
parents=df1.parents,
domain=dict(column=0)
))
fig.add_trace(go.Sunburst(
ids=df2.ids,
labels=df2.labels,
parents=df2.parents,
domain=dict(column=1),
maxdepth=2
))
fig.update_layout(
grid= dict(columns=2, rows=1),
margin = dict(t=0, l=0, r=0, b=0)
)
fig.show()
#Controlling text orientation inside sunburst sectors
import plotly.graph_objects as go
import pandas as pd
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/718417069ead87650b90472464c7565dc8c2cb1c/coffee-flavors.csv')
fig = go.Figure()
fig.add_trace(go.Sunburst(
ids=df.ids,
labels=df.labels,
parents=df.parents,
domain=dict(column=1),
maxdepth=2,
insidetextorientation='radial'
))
fig.update_layout(
margin = dict(t=10, l=10, r=10, b=10)
)
fig.show()
#Controlling text fontsize with uniformtext
import plotly.graph_objects as go
import pandas as pd
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/718417069ead87650b90472464c7565dc8c2cb1c/sunburst-coffee-flavors-complete.csv')
fig = go.Figure(go.Sunburst(
ids = df.ids,
labels = df.labels,
parents = df.parents))
fig.update_layout(uniformtext=dict(minsize=10, mode='hide'))
fig.show()
#Sunburst chart with a continuous colorscale
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import pandas as pd
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/sales_success.csv')
print(df.head())
levels = ['salesperson', 'county', 'region'] # levels used for the hierarchical chart
color_columns = ['sales', 'calls']
value_column = 'calls'
def build_hierarchical_dataframe(df, levels, value_column, color_columns=None):
"""
Build a hierarchy of levels for Sunburst or Treemap charts.
Levels are given starting from the bottom to the top of the hierarchy,
ie the last level corresponds to the root.
"""
df_all_trees = pd.DataFrame(columns=['id', 'parent', 'value', 'color'])
for i, level in enumerate(levels):
df_tree = pd.DataFrame(columns=['id', 'parent', 'value', 'color'])
dfg = df.groupby(levels[i:]).sum()
dfg = dfg.reset_index()
df_tree['id'] = dfg[level].copy()
if i < len(levels) - 1:
df_tree['parent'] = dfg[levels[i+1]].copy()
else:
df_tree['parent'] = 'total'
df_tree['value'] = dfg[value_column]
df_tree['color'] = dfg[color_columns[0]] / dfg[color_columns[1]]
df_all_trees = df_all_trees.append(df_tree, ignore_index=True)
total = pd.Series(dict(id='total', parent='',
value=df[value_column].sum(),
color=df[color_columns[0]].sum() / df[color_columns[1]].sum()))
df_all_trees = df_all_trees.append(total, ignore_index=True)
return df_all_trees
df_all_trees = build_hierarchical_dataframe(df, levels, value_column, color_columns)
average_score = df['sales'].sum() / df['calls'].sum()
fig = make_subplots(1, 2, specs=[[{"type": "domain"}, {"type": "domain"}]],)
fig.add_trace(go.Sunburst(
labels=df_all_trees['id'],
parents=df_all_trees['parent'],
values=df_all_trees['value'],
branchvalues='total',
marker=dict(
colors=df_all_trees['color'],
colorscale='RdBu',
cmid=average_score),
hovertemplate='<b>%{label} </b> <br> Sales: %{value}<br> Success rate: %{color:.2f}',
name=''
), 1, 1)
fig.add_trace(go.Sunburst(
labels=df_all_trees['id'],
parents=df_all_trees['parent'],
values=df_all_trees['value'],
branchvalues='total',
marker=dict(
colors=df_all_trees['color'],
colorscale='RdBu',
cmid=average_score),
hovertemplate='<b>%{label} </b> <br> Sales: %{value}<br> Success rate: %{color:.2f}',
maxdepth=2
), 1, 2)
fig.update_layout(margin=dict(t=10, b=10, r=10, l=10))
fig.show()
Unnamed: 0 region county salesperson calls sales 0 0 North Dallam JE 35 23 1 1 North Dallam ZQ 49 13 2 2 North Dallam IJ 20 6 3 3 North Hartley WE 39 37 4 4 North Hartley PL 42 37
C:\Users\Prosenjeet Saha\AppData\Local\Temp\ipykernel_11232\1979157041.py:32: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. C:\Users\Prosenjeet Saha\AppData\Local\Temp\ipykernel_11232\1979157041.py:32: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. C:\Users\Prosenjeet Saha\AppData\Local\Temp\ipykernel_11232\1979157041.py:32: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. C:\Users\Prosenjeet Saha\AppData\Local\Temp\ipykernel_11232\1979157041.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
import plotly.express as px
long_df = px.data.medals_long()
long_df
| nation | medal | count | |
|---|---|---|---|
| 0 | South Korea | gold | 24 |
| 1 | China | gold | 10 |
| 2 | Canada | gold | 9 |
| 3 | South Korea | silver | 13 |
| 4 | China | silver | 15 |
| 5 | Canada | silver | 12 |
| 6 | South Korea | bronze | 11 |
| 7 | China | bronze | 8 |
| 8 | Canada | bronze | 12 |
import plotly.express as px
wide_df = px.data.medals_wide()
wide_df
| nation | gold | silver | bronze | |
|---|---|---|---|---|
| 0 | South Korea | 24 | 13 | 11 |
| 1 | China | 10 | 15 | 8 |
| 2 | Canada | 9 | 12 | 12 |
import plotly.express as px
long_df = px.data.medals_long()
fig = px.bar(long_df, x="nation", y="count", color="medal", title="Long-Form Input")
fig.show()
import plotly.express as px
wide_df = px.data.medals_wide()
fig = px.bar(wide_df, x="nation", y=["gold", "silver", "bronze"], title="Wide-Form Input")
fig.show()
import plotly.express as px
wide_df = px.data.medals_wide()
fig = px.bar(wide_df, x="nation", y=["gold", "silver", "bronze"], title="Wide-Form Input, relabelled",
labels={"value": "count", "variable": "medal"})
fig.show()
import plotly.express as px
df = px.data.iris()
# Use directly Columns as argument. You can use tab completion for this!
fig = px.scatter(df, x=df.sepal_length, y=df.sepal_width, color=df.species, size=df.petal_length)
fig.show()
#Passing name strings as arguments
import plotly.express as px
df = px.data.iris()
# Use column names instead. This is the same chart as above.
fig = px.scatter(df, x='sepal_length', y='sepal_width', color='species', size='petal_length')
fig.show()
import plotly.express as px
df = px.data.iris()
fig = px.scatter(df, x=df.sepal_length, y=df.sepal_width, size=df.petal_length,
hover_data=[df.index])
fig.show()
#Columns not in the data_frame argument
import plotly.express as px
import pandas as pd
df1 = pd.DataFrame(dict(time=[10, 20, 30], sales=[10, 8, 30]))
df2 = pd.DataFrame(dict(market=[4, 2, 5]))
fig = px.bar(df1, x=df1.time, y=df2.market, color=df1.sales)
fig.show()
#Using labels to pass names
import plotly.express as px
import pandas as pd
df = px.data.gapminder()
gdp = df['pop'] * df['gdpPercap']
fig = px.bar(df, x='year', y=gdp, color='continent', labels={'y':'gdp'},
hover_data=['country'],
title='Evolution of world GDP')
fig.show()
#Input Data as array-like columns: NumPy arrays, lists
import plotly.express as px
# List arguments
fig = px.line(x=[1, 2, 3, 4], y=[3, 5, 4, 8])
fig.show()
import numpy as np
import plotly.express as px
t = np.linspace(0, 10, 100)
# NumPy arrays arguments
fig = px.scatter(x=t, y=np.sin(t), labels={'x':'t', 'y':'sin(t)'}) # override keyword names with labels
fig.show()
import plotly.express as px
# List arguments in wide form
series1 = [3, 5, 4, 8]
series2 = [5, 4, 8, 3]
fig = px.line(x=[1, 2, 3, 4], y=[series1, series2])
fig.show()
#Passing dictionaries or array-likes as the data_frame argument
#The column-based argument data_frame can also be passed with a dict or array. Using a dictionary can be a convenient way to pass column names used in axis titles, legend entries and hovers without creating a pandas DataFrame.
import plotly.express as px
import numpy as np
N = 10000
np.random.seed(0)
fig = px.density_contour(dict(effect_size=5 + np.random.randn(N),
waiting_time=np.random.poisson(size=N)),
x="effect_size", y="waiting_time")
fig.show()
#Integer column names
#When the data_frame argument is a NumPy array, column names are integer corresponding to the columns of the array. In this case, keyword names are used in axis, legend and hovers. This is also the case for a pandas DataFrame with integer column names.
#Use the labels argument to override these names.
import numpy as np
import plotly.express as px
ar = np.arange(100).reshape((10, 10))
fig = px.scatter(ar, x=2, y=6, size=1, color=5)
fig.show()
#Mixing dataframes and other types
#It is possible to mix DataFrame columns, NumPy arrays and lists as arguments.
#Remember that the only column names to be used correspond to columns in the data_frame argument,
#use labels to override names displayed in axis titles,
#legend entries or hovers.
import plotly.express as px
import numpy as np
import pandas as pd
df = px.data.gapminder()
gdp = np.log(df['pop'] * df['gdpPercap']) # NumPy array
fig = px.bar(df, x='year', y=gdp, color='continent', labels={'y':'log gdp'},
hover_data=['country'],
title='Evolution of world GDP')
fig.show()
import plotly.express as px
df = px.data.tips()
fig = px.histogram(df, x="day", y="total_bill", color="sex")
fig.show()
import plotly.express as px
df = px.data.tips()
fig = px.histogram(df, x="day", y="total_bill", color="sex",
title="Receipts by Payer Gender and Day of Week",
width=600, height=400,
labels={ # replaces default labels by column name
"sex": "Payer Gender", "day": "Day of Week", "total_bill": "Receipts"
},
category_orders={ # replaces default order by column name
"day": ["Thur", "Fri", "Sat", "Sun"], "sex": ["Male", "Female"]
},
color_discrete_map={ # replaces default color mapping by value
"Male": "RebeccaPurple", "Female": "MediumPurple"
},
template="simple_white"
)
fig.show()
import plotly.express as px
df = px.data.tips()
fig = px.histogram(df, x="day", y="total_bill", color="sex",
title="Receipts by Payer Gender and Day of Week vs Target",
width=600, height=400,
labels={"sex": "Payer Gender", "day": "Day of Week", "total_bill": "Receipts"},
category_orders={"day": ["Thur", "Fri", "Sat", "Sun"], "sex": ["Male", "Female"]},
color_discrete_map={"Male": "RebeccaPurple", "Female": "MediumPurple"},
template="simple_white"
)
fig.update_yaxes( # the y-axis is in dollars
tickprefix="$", showgrid=True
)
fig.update_layout( # customize font and legend orientation & position
font_family="Rockwell",
legend=dict(
title=None, orientation="h", y=1, yanchor="bottom", x=0.5, xanchor="center"
)
)
fig.add_shape( # add a horizontal "target" line
type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
x0=0, x1=1, xref="paper", y0=950, y1=950, yref="y"
)
fig.add_annotation( # add a text callout with arrow
text="below target!", x="Fri", y=400, arrowhead=1, showarrow=True
)
fig.show()
import plotly.express as px
import plotly.io as pio
pio.templates.default = "simple_white"
px.defaults.template = "ggplot2"
px.defaults.color_continuous_scale = px.colors.sequential.Blackbody
px.defaults.width = 600
px.defaults.height = 400
df = px.data.iris()
fig = px.scatter(df, x="sepal_width", y="sepal_length", color="sepal_length", width=400)
fig.show()
#Colored Bars
import plotly.express as px
df = px.data.gapminder().query("country == 'Canada'")
fig = px.bar(df, x='year', y='pop',
hover_data=['lifeExp', 'gdpPercap'], color='lifeExp',
labels={'pop':'population of Canada'}, height=400)
fig.show()
import plotly.express as px
df = px.data.gapminder().query("continent == 'Oceania'")
fig = px.bar(df, x='year', y='pop',
hover_data=['lifeExp', 'gdpPercap'], color='country',
labels={'pop':'population of Canada'}, height=400)
fig.show()
#Stacked vs Grouped Bars
import plotly.express as px
df = px.data.tips()
fig = px.bar(df, x="sex", y="total_bill", color='time')
fig.show()
import plotly.express as px
df = px.data.tips()
fig = px.bar(df, x="sex", y="total_bill",
color='smoker', barmode='group',
height=400)
fig.show()
#Aggregating into Single Colored Bars
import plotly.express as px
df = px.data.tips()
fig = px.histogram(df, x="sex", y="total_bill",
color='smoker', barmode='group',
height=400)
fig.show()
import plotly.express as px
df = px.data.tips()
fig = px.histogram(df, x="sex", y="total_bill",
color='smoker', barmode='group',
histfunc='avg',
height=400)
fig.show()
#Bar Charts with Text
import plotly.express as px
df = px.data.medals_long()
fig = px.bar(df, x="medal", y="count", color="nation", text_auto=True)
fig.show()
import plotly.express as px
df = px.data.medals_long()
fig = px.bar(df, x="medal", y="count", color="nation", text="nation")
fig.show()
import plotly.express as px
df = px.data.gapminder().query("continent == 'Europe' and year == 2007 and pop > 2.e6")
fig = px.bar(df, y='pop', x='country', text_auto='.2s',
title="Default: various text sizes, positions and angles")
fig.show()
import plotly.express as px
df = px.data.gapminder().query("continent == 'Europe' and year == 2007 and pop > 2.e6")
fig = px.bar(df, y='pop', x='country', text_auto='.2s',
title="Controlled text sizes, positions and angles")
fig.update_traces(textfont_size=12, textangle=0, textposition="outside", cliponaxis=False)
fig.show()
#Pattern Fills
import plotly.express as px
df = px.data.medals_long()
fig = px.bar(df, x="medal", y="count", color="nation",
pattern_shape="nation", pattern_shape_sequence=[".", "x", "+"])
fig.show()
#Facetted subplots
import plotly.express as px
df = px.data.tips()
fig = px.bar(df, x="sex", y="total_bill", color="smoker", barmode="group",
facet_row="time", facet_col="day",
category_orders={"day": ["Thur", "Fri", "Sat", "Sun"],
"time": ["Lunch", "Dinner"]})
fig.show()
#Basic Bar Charts
import plotly.graph_objects as go
animals=['giraffes', 'orangutans', 'monkeys']
fig = go.Figure([go.Bar(x=animals, y=[20, 14, 23])])
fig.show()
#Grouped Bar Chart
import plotly.graph_objects as go
animals=['giraffes', 'orangutans', 'monkeys']
fig = go.Figure(data=[
go.Bar(name='SF Zoo', x=animals, y=[20, 14, 23]),
go.Bar(name='LA Zoo', x=animals, y=[12, 18, 29])
])
# Change the bar mode
fig.update_layout(barmode='group')
fig.show()
#Stacked Bar Chart
import plotly.graph_objects as go
animals=['giraffes', 'orangutans', 'monkeys']
fig = go.Figure(data=[
go.Bar(name='SF Zoo', x=animals, y=[20, 14, 23]),
go.Bar(name='LA Zoo', x=animals, y=[12, 18, 29])
])
# Change the bar mode
fig.update_layout(barmode='stack')
fig.show()
#Bar Chart with Hover Text
import plotly.graph_objects as go
x = ['Product A', 'Product B', 'Product C']
y = [20, 14, 23]
# Use the hovertext kw argument for hover text
fig = go.Figure(data=[go.Bar(x=x, y=y,
hovertext=['27% market share', '24% market share', '19% market share'])])
# Customize aspect
fig.update_traces(marker_color='rgb(158,202,225)', marker_line_color='rgb(8,48,107)',
marker_line_width=1.5, opacity=0.6)
fig.update_layout(title_text='January 2013 Sales Report')
fig.show()
#Bar Chart with Direct Labels
import plotly.graph_objects as go
x = ['Product A', 'Product B', 'Product C']
y = [20, 14, 23]
# Use textposition='auto' for direct text
fig = go.Figure(data=[go.Bar(
x=x, y=y,
text=y,
textposition='auto',
)])
fig.show()
import plotly.express as px
df = px.data.gapminder().query("continent == 'Europe' and year == 2007 and pop > 2.e6")
fig = px.bar(df, y='pop', x='country', text='pop')
fig.update_traces(texttemplate='%{text:.2s}', textposition='outside')
fig.update_layout(uniformtext_minsize=8, uniformtext_mode='hide')
fig.show()
#Rotated Bar Chart Labels
import plotly.graph_objects as go
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',
'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
fig = go.Figure()
fig.add_trace(go.Bar(
x=months,
y=[20, 14, 25, 16, 18, 22, 19, 15, 12, 16, 14, 17],
name='Primary Product',
marker_color='indianred'
))
fig.add_trace(go.Bar(
x=months,
y=[19, 14, 22, 14, 16, 19, 15, 14, 10, 12, 12, 16],
name='Secondary Product',
marker_color='lightsalmon'
))
# Here we modify the tickangle of the xaxis, resulting in rotated labels.
fig.update_layout(barmode='group', xaxis_tickangle=-45)
fig.show()
import plotly.graph_objects as go
colors = ['lightslategray',] * 5
colors[1] = 'crimson'
fig = go.Figure(data=[go.Bar(
x=['Feature A', 'Feature B', 'Feature C',
'Feature D', 'Feature E'],
y=[20, 14, 23, 25, 22],
marker_color=colors # marker color can be a single color value or an iterable
)])
fig.update_layout(title_text='Least Used Feature')
import plotly.graph_objects as go
fig = go.Figure(data=[go.Bar(
x=[1, 2, 3, 5.5, 10],
y=[10, 8, 6, 4, 2],
width=[0.8, 0.8, 0.8, 3.5, 4] # customize width here
)])
fig.show()
import plotly.graph_objects as go
import numpy as np
labels = ["apples","oranges","pears","bananas"]
widths = np.array([10,20,20,50])
data = {
"South": [50,80,60,70],
"North": [50,20,40,30]
}
fig = go.Figure()
for key in data:
fig.add_trace(go.Bar(
name=key,
y=data[key],
x=np.cumsum(widths)-widths,
width=widths,
offset=0,
customdata=np.transpose([labels, widths*data[key]]),
texttemplate="%{y} x %{width} =<br>%{customdata[1]}",
textposition="inside",
textangle=0,
textfont_color="white",
hovertemplate="<br>".join([
"label: %{customdata[0]}",
"width: %{width}",
"height: %{y}",
"area: %{customdata[1]}",
])
))
fig.update_xaxes(
tickvals=np.cumsum(widths)-widths/2,
ticktext= ["%s<br>%d" % (l, w) for l, w in zip(labels, widths)]
)
fig.update_xaxes(range=[0,100])
fig.update_yaxes(range=[0,100])
fig.update_layout(
title_text="Marimekko Chart",
barmode="stack",
uniformtext=dict(mode="hide", minsize=10),
)
#Customizing Individual Bar Base
import plotly.graph_objects as go
years = ['2016','2017','2018']
fig = go.Figure()
fig.add_trace(go.Bar(x=years, y=[500, 600, 700],
base=[-500,-600,-700],
marker_color='crimson',
name='expenses'))
fig.add_trace(go.Bar(x=years, y=[300, 400, 700],
base=0,
marker_color='lightslategrey',
name='revenue'
))
fig.show()
#Colored and Styled Bar Chart
import plotly.graph_objects as go
years = [1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,
2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012]
fig = go.Figure()
fig.add_trace(go.Bar(x=years,
y=[219, 146, 112, 127, 124, 180, 236, 207, 236, 263,
350, 430, 474, 526, 488, 537, 500, 439],
name='Rest of world',
marker_color='rgb(55, 83, 109)'
))
fig.add_trace(go.Bar(x=years,
y=[16, 13, 10, 11, 28, 37, 43, 55, 56, 88, 105, 156, 270,
299, 340, 403, 549, 499],
name='China',
marker_color='rgb(26, 118, 255)'
))
fig.update_layout(
title='US Export of Plastic Scrap',
xaxis_tickfont_size=14,
yaxis=dict(
title='USD (millions)',
titlefont_size=16,
tickfont_size=14,
),
legend=dict(
x=0,
y=1.0,
bgcolor='rgba(255, 255, 255, 0)',
bordercolor='rgba(255, 255, 255, 0)'
),
barmode='group',
bargap=0.15, # gap between bars of adjacent location coordinates.
bargroupgap=0.1 # gap between bars of the same location coordinate.
)
fig.show()
#Bar Chart with Relative Barmode
import plotly.graph_objects as go
x = [1, 2, 3, 4]
fig = go.Figure()
fig.add_trace(go.Bar(x=x, y=[1, 4, 9, 16]))
fig.add_trace(go.Bar(x=x, y=[6, -8, -4.5, 8]))
fig.add_trace(go.Bar(x=x, y=[-15, -3, 4.5, -8]))
fig.add_trace(go.Bar(x=x, y=[-1, 3, -3, -4]))
fig.update_layout(barmode='relative', title_text='Relative Barmode')
fig.show()
#Bar Chart with Sorted or Ordered Categories
import plotly.graph_objects as go
x=['b', 'a', 'c', 'd']
fig = go.Figure(go.Bar(x=x, y=[2,5,1,9], name='Montreal'))
fig.add_trace(go.Bar(x=x, y=[1, 4, 9, 16], name='Ottawa'))
fig.add_trace(go.Bar(x=x, y=[6, 8, 4.5, 8], name='Toronto'))
fig.update_layout(barmode='stack', xaxis={'categoryorder':'category ascending'})
fig.show()
import plotly.graph_objects as go
x=['b', 'a', 'c', 'd']
fig = go.Figure(go.Bar(x=x, y=[2,5,1,9], name='Montreal'))
fig.add_trace(go.Bar(x=x, y=[1, 4, 9, 16], name='Ottawa'))
fig.add_trace(go.Bar(x=x, y=[6, 8, 4.5, 8], name='Toronto'))
fig.update_layout(barmode='stack', xaxis={'categoryorder':'array', 'categoryarray':['d','a','c','b']})
fig.show()
import plotly.graph_objects as go
x=['b', 'a', 'c', 'd']
fig = go.Figure(go.Bar(x=x, y=[2,5,1,9], name='Montreal'))
fig.add_trace(go.Bar(x=x, y=[1, 4, 9, 16], name='Ottawa'))
fig.add_trace(go.Bar(x=x, y=[6, 8, 4.5, 8], name='Toronto'))
fig.update_layout(barmode='stack', xaxis={'categoryorder':'total descending'})
fig.show()
#Bar Charts With Multicategory Axis Type
import plotly.graph_objects as go
x = [
["BB+", "BB+", "BB+", "BB", "BB", "BB"],
[16, 17, 18, 16, 17, 18,]
]
fig = go.Figure()
fig.add_bar(x=x,y=[1,2,3,4,5,6])
fig.add_bar(x=x,y=[6,5,4,3,2,1])
fig.update_layout(barmode="relative")
fig.show()
#Patterned Charts
import plotly.express as px
df = px.data.medals_long()
fig = px.bar(df, x="medal", y="count", color="nation", pattern_shape="nation")
fig.show()
import plotly.express as px
df = px.data.medals_long()
fig = px.area(df, x="medal", y="count", color="nation", pattern_shape="nation")
fig.show()
import plotly.express as px
df = px.data.tips()
fig = px.histogram(df, x="sex", y="total_bill", color="sex", pattern_shape="smoker")
fig.show()
#Controlling Pattern Assignment
import plotly.express as px
df = px.data.medals_long()
fig = px.bar(df, x="medal", y="count", color="nation",
pattern_shape="nation", pattern_shape_sequence=[".", "x", "+"])
fig.show()
import plotly.express as px
df = px.data.medals_long()
fig = px.bar(df, x="medal", y="count", color="nation",
pattern_shape="nation", pattern_shape_map={
"China": ".", "Canada": "/", "South Korea": "+"
})
fig.show()
#Black on White Patterns for Print
import plotly.express as px
df = px.data.medals_long()
fig = px.bar(df, x="medal", y="count", color="nation",
pattern_shape="nation", pattern_shape_sequence=[".", "x", "+"],
template="simple_white"
)
fig.update_traces(
marker=dict(color="black", line_color="black", pattern_fillmode="replace")
)
fig.show()
import plotly.express as px
df = px.data.medals_long()
fig = px.bar(df, x="medal", y="count", color="nation",
pattern_shape="nation", pattern_shape_sequence=[".", "x", "+"],
)
fig.update_traces(
marker=dict(line_color="grey", pattern_fillmode="replace")
)
fig.show()
import plotly.graph_objects as go
fig = go.Figure()
fig.add_trace(go.Bar(x=["a","b"], y=[1,2], marker_pattern_shape="."))
fig.add_trace(go.Bar(x=["a","b"], y=[3,1], marker_pattern_shape="x"))
fig.add_trace(go.Bar(x=["a","b"], y=[2,3], marker_pattern_shape="+"))
fig.show()
#Line plots
import plotly.express as px
import numpy as np
t = np.linspace(0, 2*np.pi, 100)
fig = px.line(x=t, y=np.cos(t), labels={'x':'t', 'y':'cos(t)'})
fig.show()
import plotly.express as px
df = px.data.gapminder().query("continent == 'Oceania'")
fig = px.line(df, x='year', y='lifeExp', color='country')
fig.show()
import plotly.express as px
df = px.data.gapminder().query("continent == 'Oceania'")
fig = px.line(df, x='year', y='lifeExp', color='country', markers=True)
fig.show()
import plotly.express as px
df = px.data.gapminder().query("continent == 'Oceania'")
fig = px.line(df, x='year', y='lifeExp', color='country', symbol="country")
fig.show()
#Line plots on Date axes
import plotly.express as px
df = px.data.stocks()
fig = px.line(df, x='date', y="GOOG")
fig.show()
#Data Order in Scatter and Line Charts
import plotly.express as px
import pandas as pd
df = pd.DataFrame(dict(
x = [1, 3, 2, 4],
y = [1, 2, 3, 4]
))
fig = px.line(df, x="x", y="y", title="Unsorted Input")
fig.show()
df = df.sort_values(by="x")
fig = px.line(df, x="x", y="y", title="Sorted Input")
fig.show()
#Connected Scatterplots
#In a connected scatterplot, two continuous variables are plotted against each other, with a line connecting them in some meaningful order, usually a time variable. In the plot below, we show the "trajectory" of a pair of countries through a space defined by GDP per Capita and Life Expectancy. Botswana's life expectancy
import plotly.express as px
df = px.data.gapminder().query("country in ['Canada', 'Botswana']")
fig = px.line(df, x="lifeExp", y="gdpPercap", color="country", text="year")
fig.update_traces(textposition="bottom right")
fig.show()
#Scatter and line plots
import plotly.graph_objects as go
import numpy as np
N = 1000
t = np.linspace(0, 10, 100)
y = np.sin(t)
fig = go.Figure(data=go.Scatter(x=t, y=y, mode='markers'))
fig.show()
import plotly.graph_objects as go
# Create random data with numpy
import numpy as np
np.random.seed(1)
N = 100
random_x = np.linspace(0, 1, N)
random_y0 = np.random.randn(N) + 5
random_y1 = np.random.randn(N)
random_y2 = np.random.randn(N) - 5
fig = go.Figure()
# Add traces
fig.add_trace(go.Scatter(x=random_x, y=random_y0,
mode='markers',
name='markers'))
fig.add_trace(go.Scatter(x=random_x, y=random_y1,
mode='lines+markers',
name='lines+markers'))
fig.add_trace(go.Scatter(x=random_x, y=random_y2,
mode='lines',
name='lines'))
fig.show()
import plotly.graph_objects as go
fig = go.Figure(data=go.Scatter(
x=[1, 2, 3, 4],
y=[10, 11, 12, 13],
mode='markers',
marker=dict(size=[40, 60, 80, 100],
color=[0, 1, 2, 3])
))
fig.show()
import plotly.graph_objects as go
import numpy as np
t = np.linspace(0, 10, 100)
fig = go.Figure()
fig.add_trace(go.Scatter(
x=t, y=np.sin(t),
name='sin',
mode='markers',
marker_color='rgba(152, 0, 0, .8)'
))
fig.add_trace(go.Scatter(
x=t, y=np.cos(t),
name='cos',
marker_color='rgba(255, 182, 193, .9)'
))
# Set options common to all traces with fig.update_traces
fig.update_traces(mode='markers', marker_line_width=2, marker_size=10)
fig.update_layout(title='Styled Scatter',
yaxis_zeroline=False, xaxis_zeroline=False)
fig.show()
#Data Labels on Hover
import plotly.graph_objects as go
import pandas as pd
data= pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/2014_usa_states.csv")
fig = go.Figure(data=go.Scatter(x=data['Postal'],
y=data['Population'],
mode='markers',
marker_color=data['Population'],
text=data['State'])) # hover text goes here
fig.update_layout(title='Population of USA States')
fig.show()
#Scatter with a Color Dimension
import plotly.graph_objects as go
import numpy as np
fig = go.Figure(data=go.Scatter(
y = np.random.randn(500),
mode='markers',
marker=dict(
size=16,
color=np.random.randn(500), #set color equal to a variable
colorscale='Viridis', # one of plotly colorscales
showscale=True
)
))
fig.show()
#Using a Slider and Buttons
import plotly.graph_objects as go
import pandas as pd
url = "https://raw.githubusercontent.com/plotly/datasets/master/gapminderDataFiveYear.csv"
dataset = pd.read_csv(url)
years = ["1952", "1962", "1967", "1972", "1977", "1982", "1987", "1992", "1997", "2002",
"2007"]
# make list of continents
continents = []
for continent in dataset["continent"]:
if continent not in continents:
continents.append(continent)
# make figure
fig_dict = {
"data": [],
"layout": {},
"frames": []
}
# fill in most of layout
fig_dict["layout"]["xaxis"] = {"range": [30, 85], "title": "Life Expectancy"}
fig_dict["layout"]["yaxis"] = {"title": "GDP per Capita", "type": "log"}
fig_dict["layout"]["hovermode"] = "closest"
fig_dict["layout"]["updatemenus"] = [
{
"buttons": [
{
"args": [None, {"frame": {"duration": 500, "redraw": False},
"fromcurrent": True, "transition": {"duration": 300,
"easing": "quadratic-in-out"}}],
"label": "Play",
"method": "animate"
},
{
"args": [[None], {"frame": {"duration": 0, "redraw": False},
"mode": "immediate",
"transition": {"duration": 0}}],
"label": "Pause",
"method": "animate"
}
],
"direction": "left",
"pad": {"r": 10, "t": 87},
"showactive": False,
"type": "buttons",
"x": 0.1,
"xanchor": "right",
"y": 0,
"yanchor": "top"
}
]
sliders_dict = {
"active": 0,
"yanchor": "top",
"xanchor": "left",
"currentvalue": {
"font": {"size": 20},
"prefix": "Year:",
"visible": True,
"xanchor": "right"
},
"transition": {"duration": 300, "easing": "cubic-in-out"},
"pad": {"b": 10, "t": 50},
"len": 0.9,
"x": 0.1,
"y": 0,
"steps": []
}
# make data
year = 1952
for continent in continents:
dataset_by_year = dataset[dataset["year"] == year]
dataset_by_year_and_cont = dataset_by_year[
dataset_by_year["continent"] == continent]
data_dict = {
"x": list(dataset_by_year_and_cont["lifeExp"]),
"y": list(dataset_by_year_and_cont["gdpPercap"]),
"mode": "markers",
"text": list(dataset_by_year_and_cont["country"]),
"marker": {
"sizemode": "area",
"sizeref": 200000,
"size": list(dataset_by_year_and_cont["pop"])
},
"name": continent
}
fig_dict["data"].append(data_dict)
# make frames
for year in years:
frame = {"data": [], "name": str(year)}
for continent in continents:
dataset_by_year = dataset[dataset["year"] == int(year)]
dataset_by_year_and_cont = dataset_by_year[
dataset_by_year["continent"] == continent]
data_dict = {
"x": list(dataset_by_year_and_cont["lifeExp"]),
"y": list(dataset_by_year_and_cont["gdpPercap"]),
"mode": "markers",
"text": list(dataset_by_year_and_cont["country"]),
"marker": {
"sizemode": "area",
"sizeref": 200000,
"size": list(dataset_by_year_and_cont["pop"])
},
"name": continent
}
frame["data"].append(data_dict)
fig_dict["frames"].append(frame)
slider_step = {"args": [
[year],
{"frame": {"duration": 300, "redraw": False},
"mode": "immediate",
"transition": {"duration": 300}}
],
"label": year,
"method": "animate"}
sliders_dict["steps"].append(slider_step)
fig_dict["layout"]["sliders"] = [sliders_dict]
fig = go.Figure(fig_dict)
fig.show()
import plotly.express as px
import pandas as pd
import numpy as np
np.random.seed(1)
N = 100000
df = pd.DataFrame(dict(x=np.random.randn(N),
y=np.random.randn(N)))
fig = px.scatter(df, x="x", y="y", render_mode='webgl')
fig.update_traces(marker_line=dict(width=1, color='DarkSlateGray'))
fig.show()
#WebGL with many traces
import plotly.graph_objects as go
import numpy as np
fig = go.Figure()
trace_num = 10
point_num = 5000
for i in range(trace_num):
fig.add_trace(
go.Scattergl(
x = np.linspace(0, 1, point_num),
y = np.random.randn(point_num)+(i*5)
)
)
fig.update_layout(showlegend=False)
fig.show()
#Box Plots in Python
import plotly.express as px
df = px.data.tips()
fig = px.box(df, y="total_bill")
fig.show()
import plotly.express as px
df = px.data.tips()
fig = px.box(df, x="time", y="total_bill")
fig.show()
#Display the underlying data
import plotly.express as px
df = px.data.tips()
fig = px.box(df, x="time", y="total_bill", points="all")
fig.show()
#Choosing The Algorithm For Computing Quartiles
import plotly.express as px
df = px.data.tips()
fig = px.box(df, x="day", y="total_bill", color="smoker")
fig.update_traces(quartilemethod="exclusive") # or "inclusive", or "linear" by default
fig.show()
#Difference Between Quartile Algorithms
import plotly.express as px
import pandas as pd
data = [1,2,3,4,5,6,7,8,9]
df = pd.DataFrame(dict(
linear=data,
inclusive=data,
exclusive=data
)).melt(var_name="quartilemethod")
fig = px.box(df, y="value", facet_col="quartilemethod", color="quartilemethod",
boxmode="overlay", points='all')
fig.update_traces(quartilemethod="linear", jitter=0, col=1)
fig.update_traces(quartilemethod="inclusive", jitter=0, col=2)
fig.update_traces(quartilemethod="exclusive", jitter=0, col=3)
fig.show()
#Styled box plot
import plotly.express as px
df = px.data.tips()
fig = px.box(df, x="time", y="total_bill", color="smoker",
notched=True, # used notched shape
title="Box plot of total bill",
hover_data=["day"] # add day column to hover data
)
fig.show()
#Basic Box Plot
import plotly.graph_objects as go
import numpy as np
np.random.seed(1)
y0 = np.random.randn(50) - 1
y1 = np.random.randn(50) + 1
fig = go.Figure()
fig.add_trace(go.Box(y=y0))
fig.add_trace(go.Box(y=y1))
fig.show()
#Basic Horizontal Box Plot
import plotly.graph_objects as go
import numpy as np
x0 = np.random.randn(50)
x1 = np.random.randn(50) + 2 # shift mean
fig = go.Figure()
# Use x instead of y argument for horizontal plot
fig.add_trace(go.Box(x=x0))
fig.add_trace(go.Box(x=x1))
fig.show()
#Box Plot That Displays The Underlying Data
import plotly.graph_objects as go
fig = go.Figure(data=[go.Box(y=[0, 1, 1, 2, 3, 5, 8, 13, 21],
boxpoints='all', # can also be outliers, or suspectedoutliers, or False
jitter=0.3, # add some jitter for a better separation between points
pointpos=-1.8 # relative position of points wrt box
)])
fig.show()
#Modifying The Algorithm For Computing Quartiles
import plotly.graph_objects as go
data = [1, 2, 3, 4, 5, 6, 7, 8, 9]
fig = go.Figure()
fig.add_trace(go.Box(y=data, quartilemethod="linear", name="Linear Quartile Mode"))
fig.add_trace(go.Box(y=data, quartilemethod="inclusive", name="Inclusive Quartile Mode"))
fig.add_trace(go.Box(y=data, quartilemethod="exclusive", name="Exclusive Quartile Mode"))
fig.update_traces(boxpoints='all', jitter=0)
fig.show()
#Box Plot With Precomputed Quartiles
import plotly.graph_objects as go
fig = go.Figure()
fig.add_trace(go.Box(y=[
[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 ],
[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 ],
[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 ]
], name="Precompiled Quartiles"))
fig.update_traces(q1=[ 1, 2, 3 ], median=[ 4, 5, 6 ],
q3=[ 7, 8, 9 ], lowerfence=[-1, 0, 1],
upperfence=[5, 6, 7], mean=[ 2.2, 2.8, 3.2 ],
sd=[ 0.2, 0.4, 0.6 ], notchspan=[ 0.2, 0.4, 0.6 ] )
fig.show()
#Colored Box Plot
import plotly.graph_objects as go
import numpy as np
y0 = np.random.randn(50)
y1 = np.random.randn(50) + 1 # shift mean
fig = go.Figure()
fig.add_trace(go.Box(y=y0, name='Sample A',
marker_color = 'indianred'))
fig.add_trace(go.Box(y=y1, name = 'Sample B',
marker_color = 'lightseagreen'))
fig.show()
#Box Plot Styling Mean & Standard Deviation
import plotly.graph_objects as go
fig = go.Figure()
fig.add_trace(go.Box(
y=[2.37, 2.16, 4.82, 1.73, 1.04, 0.23, 1.32, 2.91, 0.11, 4.51, 0.51, 3.75, 1.35, 2.98, 4.50, 0.18, 4.66, 1.30, 2.06, 1.19],
name='Only Mean',
marker_color='darkblue',
boxmean=True # represent mean
))
fig.add_trace(go.Box(
y=[2.37, 2.16, 4.82, 1.73, 1.04, 0.23, 1.32, 2.91, 0.11, 4.51, 0.51, 3.75, 1.35, 2.98, 4.50, 0.18, 4.66, 1.30, 2.06, 1.19],
name='Mean & SD',
marker_color='royalblue',
boxmean='sd' # represent mean and standard deviation
))
fig.show()
#Styling Outliers
import plotly.graph_objects as go
fig = go.Figure()
fig.add_trace(go.Box(
y=[0.75, 5.25, 5.5, 6, 6.2, 6.6, 6.80, 7.0, 7.2, 7.5, 7.5, 7.75, 8.15,
8.15, 8.65, 8.93, 9.2, 9.5, 10, 10.25, 11.5, 12, 16, 20.90, 22.3, 23.25],
name="All Points",
jitter=0.3,
pointpos=-1.8,
boxpoints='all', # represent all points
marker_color='rgb(7,40,89)',
line_color='rgb(7,40,89)'
))
fig.add_trace(go.Box(
y=[0.75, 5.25, 5.5, 6, 6.2, 6.6, 6.80, 7.0, 7.2, 7.5, 7.5, 7.75, 8.15,
8.15, 8.65, 8.93, 9.2, 9.5, 10, 10.25, 11.5, 12, 16, 20.90, 22.3, 23.25],
name="Only Whiskers",
boxpoints=False, # no data points
marker_color='rgb(9,56,125)',
line_color='rgb(9,56,125)'
))
fig.add_trace(go.Box(
y=[0.75, 5.25, 5.5, 6, 6.2, 6.6, 6.80, 7.0, 7.2, 7.5, 7.5, 7.75, 8.15,
8.15, 8.65, 8.93, 9.2, 9.5, 10, 10.25, 11.5, 12, 16, 20.90, 22.3, 23.25],
name="Suspected Outliers",
boxpoints='suspectedoutliers', # only suspected outliers
marker=dict(
color='rgb(8,81,156)',
outliercolor='rgba(219, 64, 82, 0.6)',
line=dict(
outliercolor='rgba(219, 64, 82, 0.6)',
outlierwidth=2)),
line_color='rgb(8,81,156)'
))
fig.add_trace(go.Box(
y=[0.75, 5.25, 5.5, 6, 6.2, 6.6, 6.80, 7.0, 7.2, 7.5, 7.5, 7.75, 8.15,
8.15, 8.65, 8.93, 9.2, 9.5, 10, 10.25, 11.5, 12, 16, 20.90, 22.3, 23.25],
name="Whiskers and Outliers",
boxpoints='outliers', # only outliers
marker_color='rgb(107,174,214)',
line_color='rgb(107,174,214)'
))
fig.update_layout(title_text="Box Plot Styling Outliers")
fig.show()
#Grouped Box Plots
import plotly.graph_objects as go
x = ['day 1', 'day 1', 'day 1', 'day 1', 'day 1', 'day 1',
'day 2', 'day 2', 'day 2', 'day 2', 'day 2', 'day 2']
fig = go.Figure()
fig.add_trace(go.Box(
y=[0.2, 0.2, 0.6, 1.0, 0.5, 0.4, 0.2, 0.7, 0.9, 0.1, 0.5, 0.3],
x=x,
name='kale',
marker_color='#3D9970'
))
fig.add_trace(go.Box(
y=[0.6, 0.7, 0.3, 0.6, 0.0, 0.5, 0.7, 0.9, 0.5, 0.8, 0.7, 0.2],
x=x,
name='radishes',
marker_color='#FF4136'
))
fig.add_trace(go.Box(
y=[0.1, 0.3, 0.1, 0.9, 0.6, 0.6, 0.9, 1.0, 0.3, 0.6, 0.8, 0.5],
x=x,
name='carrots',
marker_color='#FF851B'
))
fig.update_layout(
yaxis_title='normalized moisture',
boxmode='group' # group together boxes of the different traces for each value of x
)
fig.show()
#Grouped Horizontal Box Plot
import plotly.graph_objects as go
y = ['day 1', 'day 1', 'day 1', 'day 1', 'day 1', 'day 1',
'day 2', 'day 2', 'day 2', 'day 2', 'day 2', 'day 2']
fig = go.Figure()
fig.add_trace(go.Box(
x=[0.2, 0.2, 0.6, 1.0, 0.5, 0.4, 0.2, 0.7, 0.9, 0.1, 0.5, 0.3],
y=y,
name='kale',
marker_color='#3D9970'
))
fig.add_trace(go.Box(
x=[0.6, 0.7, 0.3, 0.6, 0.0, 0.5, 0.7, 0.9, 0.5, 0.8, 0.7, 0.2],
y=y,
name='radishes',
marker_color='#FF4136'
))
fig.add_trace(go.Box(
x=[0.1, 0.3, 0.1, 0.9, 0.6, 0.6, 0.9, 1.0, 0.3, 0.6, 0.8, 0.5],
y=y,
name='carrots',
marker_color='#FF851B'
))
fig.update_layout(
xaxis=dict(title='normalized moisture', zeroline=False),
boxmode='group'
)
fig.update_traces(orientation='h') # horizontal box plots
fig.show()
#Rainbow Box Plots
import plotly.graph_objects as go
import numpy as np
N = 30 # Number of boxes
# generate an array of rainbow colors by fixing the saturation and lightness of the HSL
# representation of colour and marching around the hue.
# Plotly accepts any CSS color format, see e.g. http://www.w3schools.com/cssref/css_colors_legal.asp.
c = ['hsl('+str(h)+',50%'+',50%)' for h in np.linspace(0, 360, N)]
# Each box is represented by a dict that contains the data, the type, and the colour.
# Use list comprehension to describe N boxes, each with a different colour and with different randomly generated data:
fig = go.Figure(data=[go.Box(
y=3.5 * np.sin(np.pi * i/N) + i/N + (1.5 + 0.5 * np.cos(np.pi*i/N)) * np.random.rand(10),
marker_color=c[i]
) for i in range(int(N))])
# format the layout
fig.update_layout(
xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
yaxis=dict(zeroline=False, gridcolor='white'),
paper_bgcolor='rgb(233,233,233)',
plot_bgcolor='rgb(233,233,233)',
)
fig.show()
#Fully Styled Box Plots
import plotly.graph_objects as go
x_data = ['Carmelo Anthony', 'Dwyane Wade',
'Deron Williams', 'Brook Lopez',
'Damian Lillard', 'David West',]
N = 50
y0 = (10 * np.random.randn(N) + 30).astype(np.int)
y1 = (13 * np.random.randn(N) + 38).astype(np.int)
y2 = (11 * np.random.randn(N) + 33).astype(np.int)
y3 = (9 * np.random.randn(N) + 36).astype(np.int)
y4 = (15 * np.random.randn(N) + 31).astype(np.int)
y5 = (12 * np.random.randn(N) + 40).astype(np.int)
y_data = [y0, y1, y2, y3, y4, y5]
colors = ['rgba(93, 164, 214, 0.5)', 'rgba(255, 144, 14, 0.5)', 'rgba(44, 160, 101, 0.5)',
'rgba(255, 65, 54, 0.5)', 'rgba(207, 114, 255, 0.5)', 'rgba(127, 96, 0, 0.5)']
fig = go.Figure()
for xd, yd, cls in zip(x_data, y_data, colors):
fig.add_trace(go.Box(
y=yd,
name=xd,
boxpoints='all',
jitter=0.5,
whiskerwidth=0.2,
fillcolor=cls,
marker_size=2,
line_width=1)
)
fig.update_layout(
title='Points Scored by the Top 9 Scoring NBA Players in 2012',
yaxis=dict(
autorange=True,
showgrid=True,
zeroline=True,
dtick=5,
gridcolor='rgb(255, 255, 255)',
gridwidth=1,
zerolinecolor='rgb(255, 255, 255)',
zerolinewidth=2,
),
margin=dict(
l=40,
r=30,
b=80,
t=100,
),
paper_bgcolor='rgb(243, 243, 243)',
plot_bgcolor='rgb(243, 243, 243)',
showlegend=False
)
fig.show()
C:\Users\Prosenjeet Saha\AppData\Local\Temp\ipykernel_11232\2214588450.py:10: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations C:\Users\Prosenjeet Saha\AppData\Local\Temp\ipykernel_11232\2214588450.py:11: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations C:\Users\Prosenjeet Saha\AppData\Local\Temp\ipykernel_11232\2214588450.py:12: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations C:\Users\Prosenjeet Saha\AppData\Local\Temp\ipykernel_11232\2214588450.py:13: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations C:\Users\Prosenjeet Saha\AppData\Local\Temp\ipykernel_11232\2214588450.py:14: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations C:\Users\Prosenjeet Saha\AppData\Local\Temp\ipykernel_11232\2214588450.py:15: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
#Box Plot With Only Points
import plotly.express as px
df = px.data.tips()
fig = px.strip(df, x='day', y='tip')
fig.show()
#Strip Charts in Python
import plotly.express as px
df = px.data.tips()
fig = px.strip(df, x="total_bill", y="day")
fig.show()
import plotly.express as px
df = px.data.tips()
fig = px.strip(df, x="total_bill", y="time", color="sex", facet_col="day")
fig.show()
#Filled Area Plots in Python
import plotly.express as px
df = px.data.gapminder()
fig = px.area(df, x="year", y="pop", color="continent", line_group="country")
fig.show()
#Basic Overlaid Area Chart
import plotly.graph_objects as go
fig = go.Figure()
fig.add_trace(go.Scatter(x=[1, 2, 3, 4], y=[0, 2, 3, 5], fill='tozeroy')) # fill down to xaxis
fig.add_trace(go.Scatter(x=[1, 2, 3, 4], y=[3, 5, 1, 7], fill='tonexty')) # fill to trace0 y
fig.show()
#Overlaid Area Chart Without Boundary Lines
import plotly.graph_objects as go
fig = go.Figure()
fig.add_trace(go.Scatter(x=[1, 2, 3, 4], y=[0, 2, 3, 5], fill='tozeroy',
mode='none' # override default markers+lines
))
fig.add_trace(go.Scatter(x=[1, 2, 3, 4], y=[3, 5, 1, 7], fill='tonexty',
mode= 'none'))
fig.show()
#Interior Filling for Area Chart
import plotly.graph_objects as go
fig = go.Figure()
fig.add_trace(go.Scatter(x=[1, 2, 3, 4], y=[3, 4, 8, 3],
fill=None,
mode='lines',
line_color='indigo',
))
fig.add_trace(go.Scatter(
x=[1, 2, 3, 4],
y=[1, 6, 2, 6],
fill='tonexty', # fill area between trace0 and trace1
mode='lines', line_color='indigo'))
fig.show()
#Stacked Area Chart
import plotly.graph_objects as go
x=['Winter', 'Spring', 'Summer', 'Fall']
fig = go.Figure()
fig.add_trace(go.Scatter(
x=x, y=[40, 60, 40, 10],
hoverinfo='x+y',
mode='lines',
line=dict(width=0.5, color='rgb(131, 90, 241)'),
stackgroup='one' # define stack group
))
fig.add_trace(go.Scatter(
x=x, y=[20, 10, 10, 60],
hoverinfo='x+y',
mode='lines',
line=dict(width=0.5, color='rgb(111, 231, 219)'),
stackgroup='one'
))
fig.add_trace(go.Scatter(
x=x, y=[40, 30, 50, 30],
hoverinfo='x+y',
mode='lines',
line=dict(width=0.5, color='rgb(184, 247, 212)'),
stackgroup='one'
))
fig.update_layout(yaxis_range=(0, 100))
fig.show()
#Stacked Area Chart with Normalized Values
import plotly.graph_objects as go
x=['Winter', 'Spring', 'Summer', 'Fall']
fig = go.Figure()
fig.add_trace(go.Scatter(
x=x, y=[40, 20, 30, 40],
mode='lines',
line=dict(width=0.5, color='rgb(184, 247, 212)'),
stackgroup='one',
groupnorm='percent' # sets the normalization for the sum of the stackgroup
))
fig.add_trace(go.Scatter(
x=x, y=[50, 70, 40, 60],
mode='lines',
line=dict(width=0.5, color='rgb(111, 231, 219)'),
stackgroup='one'
))
fig.add_trace(go.Scatter(
x=x, y=[70, 80, 60, 70],
mode='lines',
line=dict(width=0.5, color='rgb(127, 166, 238)'),
stackgroup='one'
))
fig.add_trace(go.Scatter(
x=x, y=[100, 100, 100, 100],
mode='lines',
line=dict(width=0.5, color='rgb(131, 90, 241)'),
stackgroup='one'
))
fig.update_layout(
showlegend=True,
xaxis_type='category',
yaxis=dict(
type='linear',
range=[1, 100],
ticksuffix='%'))
fig.show()
#Select Hover Points
import plotly.graph_objects as go
fig = go.Figure()
fig.add_trace(go.Scatter(x=[0,0.5,1,1.5,2], y=[0,1,2,1,0],
fill='toself', fillcolor='darkviolet',
hoveron = 'points+fills', # select where hover is active
line_color='darkviolet',
text="Points + Fills",
hoverinfo = 'text+x+y'))
fig.add_trace(go.Scatter(x=[3,3.5,4,4.5,5], y=[0,1,2,1,0],
fill='toself', fillcolor = 'violet',
hoveron='points',
line_color='violet',
text="Points only",
hoverinfo='text+x+y'))
fig.update_layout(
title = "hover on <i>points</i> or <i>fill</i>",
xaxis_range = [0,5.2],
yaxis_range = [0,3]
)
fig.show()
#Displaying an image and the histogram of color values
from plotly.subplots import make_subplots
from skimage import data
img = data.chelsea()
fig = make_subplots(1, 2)
# We use go.Image because subplots require traces, whereas px functions return a figure
fig.add_trace(go.Image(z=img), 1, 1)
for channel, color in enumerate(['red', 'green', 'blue']):
fig.add_trace(go.Histogram(x=img[..., channel].ravel(), opacity=0.5,
marker_color=color, name='%s channel' %color), 1, 2)
fig.update_layout(height=400)
fig.show()
#Passing image data as a binary string
import plotly.express as px
import numpy as np
img = np.arange(15**2).reshape((15, 15))
fig = px.imshow(img, binary_string=True)
fig.show()
#Exploring 3-D images, timeseries and sequences of images with facet_col
import plotly.express as px
from skimage import io
data = io.imread("https://github.com/scikit-image/skimage-tutorials/raw/main/images/cells.tif")
img = data[20:45:2]
fig = px.imshow(img, facet_col=0, binary_string=True, facet_col_wrap=5)
fig.show()
import plotly.express as px
import numpy as np
from skimage import data, filters, img_as_float
img = data.camera()
sigmas = [1, 2, 4]
img_sequence = [filters.gaussian(img, sigma=sigma) for sigma in sigmas]
fig = px.imshow(np.array(img_sequence), facet_col=0, binary_string=True,
labels={'facet_col':'sigma'})
# Set facet titles
for i, sigma in enumerate(sigmas):
fig.layout.annotations[i]['text'] = 'sigma = %d' %sigma
fig.show()
#Exploring 3-D images and timeseries with animation_frame
import plotly.express as px
from skimage import io
data = io.imread("https://github.com/scikit-image/skimage-tutorials/raw/main/images/cells.tif")
img = data[25:40]
fig = px.imshow(img, animation_frame=0, binary_string=True, labels=dict(animation_frame="slice"))
fig.show()
#Animations of xarray datasets
import plotly.express as px
import xarray as xr
# Load xarray from dataset included in the xarray tutorial
ds = xr.tutorial.open_dataset('air_temperature').air[:20]
fig = px.imshow(ds, animation_frame='time', zmin=220, zmax=300, color_continuous_scale='RdBu_r')
fig.show()
C:\Users\Prosenjeet Saha\anaconda3\lib\site-packages\paramiko\transport.py:219: CryptographyDeprecationWarning: Blowfish has been deprecated
#Read image arrays from image files
import plotly.express as px
from skimage import io
img = io.imread('https://upload.wikimedia.org/wikipedia/commons/thumb/0/00/Crab_Nebula.jpg/240px-Crab_Nebula.jpg')
fig = px.imshow(img)
fig.show()
import plotly.express as px
from skimage import data
img = data.astronaut()
fig = px.imshow(img, binary_format="jpeg", binary_compression_level=0)
fig.show()
#Display an xarray image with px.imshow
import plotly.express as px
import xarray as xr
# Load xarray from dataset included in the xarray tutorial
airtemps = xr.tutorial.open_dataset('air_temperature').air.sel(lon=250.0)
fig = px.imshow(airtemps.T, color_continuous_scale='RdBu_r', origin='lower')
fig.show()
#Scatterplot Matrix in Python
import plotly.express as px
df = px.data.iris()
fig = px.scatter_matrix(df)
fig.show()
import plotly.express as px
df = px.data.iris()
fig = px.scatter_matrix(df,
dimensions=["sepal_length", "sepal_width", "petal_length", "petal_width"],
color="species")
fig.show()
#Styled Scatter Matrix
import plotly.express as px
df = px.data.iris()
fig = px.scatter_matrix(df,
dimensions=["sepal_length", "sepal_width", "petal_length", "petal_width"],
color="species", symbol="species",
title="Scatter matrix of iris data set",
labels={col:col.replace('_', ' ') for col in df.columns}) # remove underscore
fig.update_traces(diagonal_visible=False)
fig.show()
#Splom of the Iris data set
import plotly.graph_objects as go
import pandas as pd
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/iris-data.csv')
# The Iris dataset contains four data variables, sepal length, sepal width, petal length,
# petal width, for 150 iris flowers. The flowers are labeled as `Iris-setosa`,
# `Iris-versicolor`, `Iris-virginica`.
# Define indices corresponding to flower categories, using pandas label encoding
index_vals = df['class'].astype('category').cat.codes
fig = go.Figure(data=go.Splom(
dimensions=[dict(label='sepal length',
values=df['sepal length']),
dict(label='sepal width',
values=df['sepal width']),
dict(label='petal length',
values=df['petal length']),
dict(label='petal width',
values=df['petal width'])],
text=df['class'],
marker=dict(color=index_vals,
showscale=False, # colors encode categorical variables
line_color='white', line_width=0.5)
))
fig.update_layout(
title='Iris Data set',
dragmode='select',
width=600,
height=600,
hovermode='closest',
)
fig.show()
import plotly.graph_objects as go
import pandas as pd
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/iris-data.csv')
index_vals = df['class'].astype('category').cat.codes
fig = go.Figure(data=go.Splom(
dimensions=[dict(label='sepal length',
values=df['sepal length']),
dict(label='sepal width',
values=df['sepal width']),
dict(label='petal length',
values=df['petal length']),
dict(label='petal width',
values=df['petal width'])],
diagonal_visible=False, # remove plots on diagonal
text=df['class'],
marker=dict(color=index_vals,
showscale=False, # colors encode categorical variables
line_color='white', line_width=0.5)
))
fig.update_layout(
title='Iris Data set',
width=600,
height=600,
)
fig.show()
#To plot only the lower/upper half of the splom we switch the default showlowerhalf=True/showupperhalf=True to False:
import plotly.graph_objects as go
import pandas as pd
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/iris-data.csv')
index_vals = df['class'].astype('category').cat.codes
fig = go.Figure(data=go.Splom(
dimensions=[dict(label='sepal length',
values=df['sepal length']),
dict(label='sepal width',
values=df['sepal width']),
dict(label='petal length',
values=df['petal length']),
dict(label='petal width',
values=df['petal width'])],
showupperhalf=False, # remove plots on diagonal
text=df['class'],
marker=dict(color=index_vals,
showscale=False, # colors encode categorical variables
line_color='white', line_width=0.5)
))
fig.update_layout(
title='Iris Data set',
width=600,
height=600,
)
fig.show()
#Splom for the diabetes dataset
import plotly.graph_objs as go
import pandas as pd
dfd = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/diabetes.csv')
textd = ['non-diabetic' if cl==0 else 'diabetic' for cl in dfd['Outcome']]
fig = go.Figure(data=go.Splom(
dimensions=[dict(label='Pregnancies', values=dfd['Pregnancies']),
dict(label='Glucose', values=dfd['Glucose']),
dict(label='BloodPressure', values=dfd['BloodPressure']),
dict(label='SkinThickness', values=dfd['SkinThickness']),
dict(label='Insulin', values=dfd['Insulin']),
dict(label='BMI', values=dfd['BMI']),
dict(label='DiabPedigreeFun', values=dfd['DiabetesPedigreeFunction']),
dict(label='Age', values=dfd['Age'])],
marker=dict(color=dfd['Outcome'],
size=5,
colorscale='Bluered',
line=dict(width=0.5,
color='rgb(230,230,230)')),
text=textd,
diagonal=dict(visible=False)))
title = "Scatterplot Matrix (SPLOM) for Diabetes Dataset<br>Data source:"+\
" <a href='https://www.kaggle.com/uciml/pima-indians-diabetes-database/data'>[1]</a>"
fig.update_layout(title=title,
dragmode='select',
width=1000,
height=1000,
hovermode='closest')
fig.show()
#Empirical Cumulative Distribution Plots in Python
#Simple ECDF Plots
import plotly.express as px
df = px.data.tips()
fig = px.ecdf(df, x="total_bill")
fig.show()
import plotly.express as px
df = px.data.tips()
fig = px.ecdf(df, x=["total_bill", "tip"])
fig.show()
import plotly.express as px
df = px.data.tips()
fig = px.ecdf(df, x="total_bill", color="sex")
fig.show()
#Configuring the Y axis
import plotly.express as px
df = px.data.tips()
fig = px.ecdf(df, x="total_bill", color="sex", ecdfnorm=None)
fig.show()
import plotly.express as px
df = px.data.tips()
fig = px.ecdf(df, x="total_bill", y="tip", color="sex", ecdfnorm=None)
fig.show()
#Reversed and Complementary CDF plots
import plotly.express as px
fig = px.ecdf(df, x=[1,2,3,4], markers=True, ecdfmode="standard",
title="ecdfmode='standard' (Y=fraction at or below X value, this the default)")
fig.show()
#In reversed mode, the right-most point is at 1 (or the total count/sum, depending on ecdfnorm) and the left-most point is above 0.
import plotly.express as px
fig = px.ecdf(df, x=[1,2,3,4], markers=True, ecdfmode="reversed",
title="ecdfmode='reversed' (Y=fraction at or above X value)")
fig.show()
import plotly.express as px
fig = px.ecdf(df, x=[1,2,3,4], markers=True, ecdfmode="complementary",
title="ecdfmode='complementary' (Y=fraction above X value)")
fig.show()
#Orientation
import plotly.express as px
df = px.data.tips()
fig = px.ecdf(df, x="total_bill", y="tip", color="sex", ecdfnorm=None, orientation="h")
fig.show()
#Markers and/or Lines
import plotly.express as px
df = px.data.tips()
fig = px.ecdf(df, x="total_bill", color="sex", markers=True)
fig.show()
import plotly.express as px
df = px.data.tips()
fig = px.ecdf(df, x="total_bill", color="sex", markers=True, lines=False)
fig.show()
#Marginal Plots
import plotly.express as px
df = px.data.tips()
fig = px.ecdf(df, x="total_bill", color="sex", markers=True, lines=False, marginal="histogram")
fig.show()
import plotly.express as px
df = px.data.tips()
fig = px.ecdf(df, x="total_bill", color="sex", marginal="rug")
fig.show()
#Facets
import plotly.express as px
df = px.data.tips()
fig = px.ecdf(df, x="total_bill", color="sex", facet_row="time", facet_col="day")
fig.show()
#2D Histogram Contour in Python
import plotly.express as px
df = px.data.tips()
fig = px.density_contour(df, x="total_bill", y="tip")
fig.show()
import plotly.express as px
df = px.data.tips()
fig = px.density_contour(df, x="total_bill", y="tip", marginal_x="histogram", marginal_y="histogram")
fig.show()
import plotly.express as px
df = px.data.tips()
fig = px.density_contour(df, x="total_bill", y="tip", facet_col="sex", color="smoker")
fig.show()
import plotly.express as px
df = px.data.tips()
fig = px.density_contour(df, x="total_bill", y="tip")
fig.update_traces(contours_coloring="fill", contours_showlabels = True)
fig.show()
import plotly.express as px
df = px.data.iris()
fig = px.density_contour(df, x="petal_length", y="petal_width", z="sepal_length", histfunc="avg")
fig.show()
#Basic 2D Histogram Contour
import plotly.graph_objects as go
import numpy as np
np.random.seed(1)
x = np.random.uniform(-1, 1, size=500)
y = np.random.uniform(-1, 1, size=500)
fig = go.Figure(go.Histogram2dContour(
x = x,
y = y
))
fig.show()
#2D Histogram Contour Colorscale
import plotly.graph_objects as go
import numpy as np
x = np.random.uniform(-1, 1, size=500)
y = np.random.uniform(-1, 1, size=500)
fig = go.Figure(go.Histogram2dContour(
x = x,
y = y,
colorscale = 'Blues'
))
fig.show()
import plotly.graph_objects as go
import numpy as np
x = np.random.uniform(-1, 1, size=500)
y = np.random.uniform(-1, 1, size=500)
fig = go.Figure(go.Histogram2dContour(
x = x,
y = y,
colorscale = 'Jet',
contours = dict(
showlabels = True,
labelfont = dict(
family = 'Raleway',
color = 'white'
)
),
hoverlabel = dict(
bgcolor = 'white',
bordercolor = 'black',
font = dict(
family = 'Raleway',
color = 'black'
)
)
))
fig.show()
#2D Histogram Contour Subplot
import plotly.graph_objects as go
import numpy as np
t = np.linspace(-1, 1.2, 2000)
x = (t**3) + (0.3 * np.random.randn(2000))
y = (t**6) + (0.3 * np.random.randn(2000))
fig = go.Figure()
fig.add_trace(go.Histogram2dContour(
x = x,
y = y,
colorscale = 'Blues',
reversescale = True,
xaxis = 'x',
yaxis = 'y'
))
fig.add_trace(go.Scatter(
x = x,
y = y,
xaxis = 'x',
yaxis = 'y',
mode = 'markers',
marker = dict(
color = 'rgba(0,0,0,0.3)',
size = 3
)
))
fig.add_trace(go.Histogram(
y = y,
xaxis = 'x2',
marker = dict(
color = 'rgba(0,0,0,1)'
)
))
fig.add_trace(go.Histogram(
x = x,
yaxis = 'y2',
marker = dict(
color = 'rgba(0,0,0,1)'
)
))
fig.update_layout(
autosize = False,
xaxis = dict(
zeroline = False,
domain = [0,0.85],
showgrid = False
),
yaxis = dict(
zeroline = False,
domain = [0,0.85],
showgrid = False
),
xaxis2 = dict(
zeroline = False,
domain = [0.85,1],
showgrid = False
),
yaxis2 = dict(
zeroline = False,
domain = [0.85,1],
showgrid = False
),
height = 600,
width = 600,
bargap = 0,
hovermode = 'closest',
showlegend = False
)
fig.show()
#Ridgeline plot
import plotly.graph_objects as go
from plotly.colors import n_colors
import numpy as np
np.random.seed(1)
# 12 sets of normal distributed random data, with increasing mean and standard deviation
data = (np.linspace(1, 2, 12)[:, np.newaxis] * np.random.randn(12, 200) +
(np.arange(12) + 2 * np.random.random(12))[:, np.newaxis])
colors = n_colors('rgb(5, 200, 200)', 'rgb(200, 10, 10)', 12, colortype='rgb')
fig = go.Figure()
for data_line, color in zip(data, colors):
fig.add_trace(go.Violin(x=data_line, line_color=color))
fig.update_traces(orientation='h', side='positive', width=3, points=False)
fig.update_layout(xaxis_showgrid=False, xaxis_zeroline=False)
fig.show()
#Advanced Violin Plot
import plotly.graph_objects as go
import pandas as pd
df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/violin_data.csv")
pointpos_male = [-0.9,-1.1,-0.6,-0.3]
pointpos_female = [0.45,0.55,1,0.4]
show_legend = [True,False,False,False]
fig = go.Figure()
for i in range(0,len(pd.unique(df['day']))):
fig.add_trace(go.Violin(x=df['day'][(df['sex'] == 'Male') &
(df['day'] == pd.unique(df['day'])[i])],
y=df['total_bill'][(df['sex'] == 'Male')&
(df['day'] == pd.unique(df['day'])[i])],
legendgroup='M', scalegroup='M', name='M',
side='negative',
pointpos=pointpos_male[i], # where to position points
line_color='lightseagreen',
showlegend=show_legend[i])
)
fig.add_trace(go.Violin(x=df['day'][(df['sex'] == 'Female') &
(df['day'] == pd.unique(df['day'])[i])],
y=df['total_bill'][(df['sex'] == 'Female')&
(df['day'] == pd.unique(df['day'])[i])],
legendgroup='F', scalegroup='F', name='F',
side='positive',
pointpos=pointpos_female[i],
line_color='mediumpurple',
showlegend=show_legend[i])
)
# update characteristics shared by all traces
fig.update_traces(meanline_visible=True,
points='all', # show all points
jitter=0.05, # add some jitter on points for better visibility
scalemode='count') #scale violin plot area with total count
fig.update_layout(
title_text="Total bill distribution<br><i>scaled by number of bills per gender",
violingap=0, violingroupgap=0, violinmode='overlay')
fig.show()
#Basic Violin Plot
import plotly.express as px
df = px.data.tips()
fig = px.violin(df, y="total_bill")
fig.show()
#Violin plot with box and data points
import plotly.express as px
df = px.data.tips()
fig = px.violin(df, y="total_bill", box=True, # draw box plot inside the violin
points='all', # can be 'outliers', or False
)
fig.show()
#Multiple Violin Plots
import plotly.express as px
df = px.data.tips()
fig = px.violin(df, y="tip", x="smoker", color="sex", box=True, points="all",
hover_data=df.columns)
fig.show()
import plotly.express as px
df = px.data.tips()
fig = px.violin(df, y="tip", color="sex",
violinmode='overlay', # draw violins on top of each other
# default violinmode is 'group' as in example above
hover_data=df.columns)
fig.show()
import plotly.graph_objects as go
import pandas as pd
df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/violin_data.csv")
fig = go.Figure(data=go.Violin(y=df['total_bill'], box_visible=True, line_color='black',
meanline_visible=True, fillcolor='lightseagreen', opacity=0.6,
x0='Total Bill'))
fig.update_layout(yaxis_zeroline=False)
fig.show()
import plotly.graph_objects as go
import pandas as pd
df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/violin_data.csv")
fig = go.Figure()
days = ['Thur', 'Fri', 'Sat', 'Sun']
for day in days:
fig.add_trace(go.Violin(x=df['day'][df['day'] == day],
y=df['total_bill'][df['day'] == day],
name=day,
box_visible=True,
meanline_visible=True))
fig.show()
#Grouped Violin Plot
import plotly.graph_objects as go
import pandas as pd
df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/violin_data.csv")
fig = go.Figure()
fig.add_trace(go.Violin(x=df['day'][ df['sex'] == 'Male' ],
y=df['total_bill'][ df['sex'] == 'Male' ],
legendgroup='M', scalegroup='M', name='M',
line_color='blue')
)
fig.add_trace(go.Violin(x=df['day'][ df['sex'] == 'Female' ],
y=df['total_bill'][ df['sex'] == 'Female' ],
legendgroup='F', scalegroup='F', name='F',
line_color='orange')
)
fig.update_traces(box_visible=True, meanline_visible=True)
fig.update_layout(violinmode='group')
fig.show()
#Split Violin Plot
import plotly.graph_objects as go
import pandas as pd
df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/violin_data.csv")
fig = go.Figure()
fig.add_trace(go.Violin(x=df['day'][ df['smoker'] == 'Yes' ],
y=df['total_bill'][ df['smoker'] == 'Yes' ],
legendgroup='Yes', scalegroup='Yes', name='Yes',
side='negative',
line_color='blue')
)
fig.add_trace(go.Violin(x=df['day'][ df['smoker'] == 'No' ],
y=df['total_bill'][ df['smoker'] == 'No' ],
legendgroup='No', scalegroup='No', name='No',
side='positive',
line_color='orange')
)
fig.update_traces(meanline_visible=True)
fig.update_layout(violingap=0, violinmode='overlay')
fig.show()
#Mapbox Choropleth Maps in Python
from urllib.request import urlopen
import json
with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json') as response:
counties = json.load(response)
counties["features"][0]
{'type': 'Feature',
'properties': {'GEO_ID': '0500000US01001',
'STATE': '01',
'COUNTY': '001',
'NAME': 'Autauga',
'LSAD': 'County',
'CENSUSAREA': 594.436},
'geometry': {'type': 'Polygon',
'coordinates': [[[-86.496774, 32.344437],
[-86.717897, 32.402814],
[-86.814912, 32.340803],
[-86.890581, 32.502974],
[-86.917595, 32.664169],
[-86.71339, 32.661732],
[-86.714219, 32.705694],
[-86.413116, 32.707386],
[-86.411172, 32.409937],
[-86.496774, 32.344437]]]},
'id': '01001'}
#Data indexed by id
import pandas as pd
df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv",
dtype={"fips": str})
df.head()
| fips | unemp | |
|---|---|---|
| 0 | 01001 | 5.3 |
| 1 | 01003 | 5.4 |
| 2 | 01005 | 8.6 |
| 3 | 01007 | 6.6 |
| 4 | 01009 | 5.5 |
#Choropleth map using plotly.express and carto base map (no token needed)
from urllib.request import urlopen
import json
with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json') as response:
counties = json.load(response)
import pandas as pd
df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv",
dtype={"fips": str})
import plotly.express as px
fig = px.choropleth_mapbox(df, geojson=counties, locations='fips', color='unemp',
color_continuous_scale="Viridis",
range_color=(0, 12),
mapbox_style="carto-positron",
zoom=3, center = {"lat": 37.0902, "lon": -95.7129},
opacity=0.5,
labels={'unemp':'unemployment rate'}
)
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()
#Indexing by GeoJSON Properties
import plotly.express as px
df = px.data.election()
geojson = px.data.election_geojson()
print(df["district"][2])
print(geojson["features"][0]["properties"])
11-Sault-au-Récollet
{'district': '11-Sault-au-Récollet'}
import plotly.express as px
df = px.data.election()
geojson = px.data.election_geojson()
fig = px.choropleth_mapbox(df, geojson=geojson, color="Bergeron",
locations="district", featureidkey="properties.district",
center={"lat": 45.5517, "lon": -73.7073},
mapbox_style="carto-positron", zoom=9)
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()
#Discrete Colors
import plotly.express as px
df = px.data.election()
geojson = px.data.election_geojson()
fig = px.choropleth_mapbox(df, geojson=geojson, color="winner",
locations="district", featureidkey="properties.district",
center={"lat": 45.5517, "lon": -73.7073},
mapbox_style="carto-positron", zoom=9)
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()
#Using GeoPandas Data Frames
import plotly.express as px
import geopandas as gpd
df = px.data.election()
geo_df = gpd.GeoDataFrame.from_features(
px.data.election_geojson()["features"]
).merge(df, on="district").set_index("district")
fig = px.choropleth_mapbox(geo_df,
geojson=geo_df.geometry,
locations=geo_df.index,
color="Joly",
center={"lat": 45.5517, "lon": -73.7073},
mapbox_style="open-street-map",
zoom=8.5)
fig.show()
#Choropleth map using plotly.graph_objects and carto base map (no token needed)
from urllib.request import urlopen
import json
with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json') as response:
counties = json.load(response)
import pandas as pd
df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv",
dtype={"fips": str})
import plotly.graph_objects as go
fig = go.Figure(go.Choroplethmapbox(geojson=counties, locations=df.fips, z=df.unemp,
colorscale="Viridis", zmin=0, zmax=12,
marker_opacity=0.5, marker_line_width=0))
fig.update_layout(mapbox_style="carto-positron",
mapbox_zoom=3, mapbox_center = {"lat": 37.0902, "lon": -95.7129})
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()
#Scatter Plots on Maps in Python
import plotly.express as px
df = px.data.gapminder().query("year == 2007")
fig = px.scatter_geo(df, locations="iso_alpha",
size="pop", # size of markers, "pop" is one of the columns of gapminder
)
fig.show()
import plotly.express as px
df = px.data.gapminder().query("year == 2007")
fig = px.scatter_geo(df, locations="iso_alpha",
color="continent", # which column to use to set the color of markers
hover_name="country", # column added to hover information
size="pop", # size of markers
projection="natural earth")
fig.show()
#U.S. Airports Map
#Simple U.S. Airports Map
import plotly.graph_objects as go
import pandas as pd
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_february_us_airport_traffic.csv')
df['text'] = df['airport'] + '' + df['city'] + ', ' + df['state'] + '' + 'Arrivals: ' + df['cnt'].astype(str)
fig = go.Figure(data=go.Scattergeo(
lon = df['long'],
lat = df['lat'],
text = df['text'],
mode = 'markers',
marker_color = df['cnt'],
))
fig.update_layout(
title = 'Most trafficked US airports<br>(Hover for airport names)',
geo_scope='usa',
)
fig.show()
#Styled U.S. Airports Map
import plotly.graph_objects as go
import pandas as pd
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_february_us_airport_traffic.csv')
df['text'] = df['airport'] + '' + df['city'] + ', ' + df['state'] + '' + 'Arrivals: ' + df['cnt'].astype(str)
fig = go.Figure(data=go.Scattergeo(
locationmode = 'USA-states',
lon = df['long'],
lat = df['lat'],
text = df['text'],
mode = 'markers',
marker = dict(
size = 8,
opacity = 0.8,
reversescale = True,
autocolorscale = False,
symbol = 'square',
line = dict(
width=1,
color='rgba(102, 102, 102)'
),
colorscale = 'Blues',
cmin = 0,
color = df['cnt'],
cmax = df['cnt'].max(),
colorbar_title="Incoming flights<br>February 2011"
)))
fig.update_layout(
title = 'Most trafficked US airports<br>(Hover for airport names)',
geo = dict(
scope='usa',
projection_type='albers usa',
showland = True,
landcolor = "rgb(250, 250, 250)",
subunitcolor = "rgb(217, 217, 217)",
countrycolor = "rgb(217, 217, 217)",
countrywidth = 0.5,
subunitwidth = 0.5
),
)
fig.show()
#Wind Rose and Polar Bar Charts in Python
import plotly.express as px
df = px.data.wind()
fig = px.bar_polar(df, r="frequency", theta="direction",
color="strength", template="plotly_dark",
color_discrete_sequence= px.colors.sequential.Plasma_r)
fig.show()
#Basic Wind Rose Chart
import plotly.graph_objects as go
fig = go.Figure()
fig.add_trace(go.Barpolar(
r=[77.5, 72.5, 70.0, 45.0, 22.5, 42.5, 40.0, 62.5],
name='11-14 m/s',
marker_color='rgb(106,81,163)'
))
fig.add_trace(go.Barpolar(
r=[57.5, 50.0, 45.0, 35.0, 20.0, 22.5, 37.5, 55.0],
name='8-11 m/s',
marker_color='rgb(158,154,200)'
))
fig.add_trace(go.Barpolar(
r=[40.0, 30.0, 30.0, 35.0, 7.5, 7.5, 32.5, 40.0],
name='5-8 m/s',
marker_color='rgb(203,201,226)'
))
fig.add_trace(go.Barpolar(
r=[20.0, 7.5, 15.0, 22.5, 2.5, 2.5, 12.5, 22.5],
name='< 5 m/s',
marker_color='rgb(242,240,247)'
))
fig.update_traces(text=['North', 'N-E', 'East', 'S-E', 'South', 'S-W', 'West', 'N-W'])
fig.update_layout(
title='Wind Speed Distribution in Laurel, NE',
font_size=16,
legend_font_size=16,
polar_radialaxis_ticksuffix='%',
polar_angularaxis_rotation=90,
)
fig.show()
import plotly.express as px
df = px.data.iris()
fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species", marginal_y="violin",
marginal_x="box", trendline="ols", template="simple_white")
fig.show()
import plotly.express as px
df = px.data.tips()
fig = px.parallel_categories(df, color="size", color_continuous_scale=px.colors.sequential.Inferno)
fig.show()
import plotly.express as px
import numpy as np
df = px.data.gapminder().query("year == 2007")
fig = px.treemap(df, path=[px.Constant('world'), 'continent', 'country'], values='pop',
color='lifeExp', hover_data=['iso_alpha'])
fig.show()
import plotly.express as px
df = px.data.tips()
fig = px.histogram(df, x="total_bill", y="tip", color="sex", marginal="rug", hover_data=df.columns)
fig.show()
import plotly.express as px
df = px.data.gapminder()
fig = px.scatter_geo(df, locations="iso_alpha", color="continent", hover_name="country", size="pop",
animation_frame="year", projection="natural earth")
fig.show()
import plotly.express as px
df = px.data.gapminder()
fig = px.choropleth(df, locations="iso_alpha", color="lifeExp", hover_name="country", animation_frame="year", range_color=[20,80])
fig.show()
import plotly.express as px
df = px.data.wind()
fig = px.scatter_polar(df, r="frequency", theta="direction", color="strength", symbol="strength",
color_discrete_sequence=px.colors.sequential.Plasma_r)
fig.show()
import plotly.express as px
df = px.data.wind()
fig = px.line_polar(df, r="frequency", theta="direction", color="strength", line_close=True,
color_discrete_sequence=px.colors.sequential.Plasma_r)
fig.show()
C:\Users\Prosenjeet Saha\anaconda3\lib\site-packages\plotly\express\_core.py:271: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. C:\Users\Prosenjeet Saha\anaconda3\lib\site-packages\plotly\express\_core.py:271: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. C:\Users\Prosenjeet Saha\anaconda3\lib\site-packages\plotly\express\_core.py:271: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. C:\Users\Prosenjeet Saha\anaconda3\lib\site-packages\plotly\express\_core.py:271: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. C:\Users\Prosenjeet Saha\anaconda3\lib\site-packages\plotly\express\_core.py:271: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. C:\Users\Prosenjeet Saha\anaconda3\lib\site-packages\plotly\express\_core.py:271: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. C:\Users\Prosenjeet Saha\anaconda3\lib\site-packages\plotly\express\_core.py:271: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. C:\Users\Prosenjeet Saha\anaconda3\lib\site-packages\plotly\express\_core.py:271: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
import plotly.express as px
df = px.data.wind()
fig = px.bar_polar(df, r="frequency", theta="direction", color="strength", template="plotly_dark",
color_discrete_sequence= px.colors.sequential.Plasma_r)
fig.show()
#Adding Shapes to Subplots
import plotly.graph_objects as go
from plotly.subplots import make_subplots
# Create Subplots
fig = make_subplots(rows=2, cols=2)
fig.add_trace(go.Scatter(x=[2, 6], y=[1,1]), row=1, col=1)
fig.add_trace(go.Bar(x=[1,2,3], y=[4,5,6]), row=1, col=2)
fig.add_trace(go.Scatter(x=[10,20], y=[40,50]), row=2, col=1)
fig.add_trace(go.Bar(x=[11,13,15], y=[8,11,20]), row=2, col=2)
# Add shapes
fig.update_layout(
shapes=[
dict(type="line", xref="x", yref="y",
x0=3, y0=0.5, x1=5, y1=0.8, line_width=3),
dict(type="rect", xref="x2", yref='y2',
x0=4, y0=2, x1=5, y1=6),
dict(type="rect", xref="x3", yref="y3",
x0=10, y0=20, x1=15, y1=30),
dict(type="circle", xref="x4", yref="y4",
x0=5, y0=12, x1=10, y1=18)])
fig.show()
#Adding the Same Shapes to Multiple Subplots
import plotly.express as px
df = px.data.tips()
fig = px.scatter(df, x="total_bill", y="tip", facet_row="smoker", facet_col="sex")
# Adds a rectangle to all facets
fig.add_shape(
dict(type="rect", x0=25, x1=35, y0=4, y1=6, line_color="purple"),
row="all",
col="all",
)
# Adds a line to all the rows of the second column
fig.add_shape(
dict(type="line", x0=20, x1=25, y0=5, y1=6, line_color="yellow"), row="all", col=2
)
# Adds a circle to all the columns of the first row
fig.add_shape(
dict(type="circle", x0=10, y0=2, x1=20, y1=7), row=1, col="all", line_color="green"
)
fig.show()
#Drawing shapes with a Mouse on Cartesian plots
import plotly.graph_objects as go
fig = go.Figure()
text="Click and drag here <br> to draw a rectangle <br><br> or select another shape <br>in the modebar"
fig.add_annotation(
x=0.5,
y=0.5,
text=text,
xref="paper",
yref="paper",
showarrow=False,
font_size=20
)
# shape defined programatically
fig.add_shape(editable=True,
x0=-1, x1=0, y0=2, y1=3,
xref='x', yref='y')
# define dragmode and add modebar buttons
fig.update_layout(dragmode='drawrect')
fig.show(config={'modeBarButtonsToAdd':['drawline',
'drawopenpath',
'drawclosedpath',
'drawcircle',
'drawrect',
'eraseshape'
]})
#Highlighting Clusters of Scatter Points with Circle Shapes
import plotly.graph_objects as go
import numpy as np
np.random.seed(1)
# Generate data
x0 = np.random.normal(2, 0.45, 300)
y0 = np.random.normal(2, 0.45, 300)
x1 = np.random.normal(6, 0.4, 200)
y1 = np.random.normal(6, 0.4, 200)
# Create figure
fig = go.Figure()
# Add scatter traces
fig.add_trace(go.Scatter(x=x0, y=y0, mode="markers"))
fig.add_trace(go.Scatter(x=x1, y=y1, mode="markers"))
# Add shapes
fig.add_shape(type="circle",
xref="x", yref="y",
x0=min(x0), y0=min(y0),
x1=max(x0), y1=max(y0),
opacity=0.2,
fillcolor="blue",
line_color="blue",
)
fig.add_shape(type="circle",
xref="x", yref="y",
x0=min(x1), y0=min(y1),
x1=max(x1), y1=max(y1),
opacity=0.2,
fillcolor="orange",
line_color="orange",
)
# Hide legend
fig.update_layout(showlegend=False)
fig.show()